Double-layer optimization method and system for participation of new storage complex in two-stage electricity market

Abstract

The invention discloses a double-layer optimization method and system for a new storage complex to participate in a two-stage electricity market, and relates to the technical field of electricity transaction, and the method comprises the steps: obtaining the prediction data of wind power and photovoltaic power, market data, and the state of a hydropower unit in an inter-province and intra-province two-stage electricity market; constructing a lower-layer optimization model with the net income maximization of the new storage complex as a target, constructing an upper-layer optimization model with the minimization of the power purchase cost and the CVaR risk cost as a target, converting and combining the lower-layer optimization model to the upper-layer optimization model to form a single-layer MILP model, and outputting an optimal scheduling plan; identifying the potential execution deviation of the medium and long term contract based on the prediction data before operation, and executing a corresponding contract adjustment strategy; and after the settlement period is finished, reconstructing a profit distribution scene based on the actual operation data, and obtaining the income of each member. According to the invention, a risk management mechanism is introduced, so that an operation strategy can effectively avoid a huge loss risk caused by extreme electricity price fluctuation while pursuing high income.

Description

Technical Field

[0001] This invention relates to the field of power trading technology, and in particular to a two-tier optimization method and system for new energy storage consortia to participate in the operation of two-tier power markets. Background Technology

[0002] Electricity resources and load centers exhibit a significant "reverse distribution" characteristic. Electricity production bases are mainly concentrated in the western and northern regions (energy-rich areas), while electricity load centers have long remained in the central and eastern regions (economically developed areas). Considering the current distribution of electricity resources and the development trend of industrial restructuring, the scale and distance of future electricity flows will further increase, exacerbating this contradiction of reverse supply and demand distribution.

[0003] Especially for the Northwest region, a crucial new energy base in my country, its wind and solar energy resources are extremely abundant. In recent years, with the rapid growth of installed capacity, the Northwest region has frequently experienced a "new energy boom," meaning that during periods of favorable wind and solar power resources, the amount of new energy power generated increases dramatically, far exceeding the local load absorption capacity. However, limited by local market space and peak-shaving capacity, the efficient transmission and consumption of new energy faces significant challenges.

[0004] To alleviate the growing contradiction between power supply and demand and to achieve optimal allocation of power resources on a larger scale, it is essential to carry out large-scale "inter-regional and inter-provincial" power trading. Currently, my country has initially established a relatively comprehensive inter-provincial power trading mechanism. With the deepening of power market reform, the comprehensive pilot programs for power system reform issued by various provinces all propose the establishment of intra-provincial power markets as their goal.

[0005] Existing research indicates that new energy storage consortia include different stakeholders (wind power, photovoltaic groups, and hydropower groups). When these consortia operate jointly in different levels of electricity markets, they use an optimized dispatch scheme based on a single market level or a single time scale. This optimized dispatch scheme allocates resources based on the principle of maximizing the interests of each stakeholder. When facing the risks brought about by fluctuations in intra-provincial or inter-provincial transaction prices, because each stakeholder needs to maximize their own interests, the dispatch scheme is unable to achieve the optimal allocation of various dispatch resources according to the risks, and the overall market is prone to significant losses. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of the prior art by providing a two-tier optimization method and system for new energy storage consortia to participate in a two-tier electricity market, thereby solving the problems in the prior art.

[0007] The present invention specifically provides the following technical solution: A two-tier optimization method for new energy storage consortia to participate in a two-tier electricity market includes: Acquire forecast data on wind power and photovoltaic power output, market data, and hydropower unit status in the inter-provincial and intra-provincial two-tier electricity markets; wherein, the market data includes historical clearing prices, tie-line transmission capacity, and load forecast results of the inter-provincial and intra-provincial electricity markets; A lower-level optimization model is constructed with the goal of maximizing the net profit of the new energy storage consortium, and an upper-level optimization model is constructed with the goal of minimizing the cost of electricity purchase and the risk cost of conditional value of risk (CVaR). The lower-level optimization model is transformed and merged into the upper-level optimization model to obtain a single-level MILP model. Market data and the unit status of hydropower are input into the single-level MILP model to output the optimal scheduling plan. The new energy storage consortium refers to an energy system that coordinates the scheduling of wind power, photovoltaic power and hydropower. Before the optimal scheduling plan is implemented, potential execution deviations in medium- and long-term contracts are identified using wind power and solar power forecast data. Corresponding contract adjustment strategies are then implemented based on these potential execution deviations to obtain the optimal scheduling plan after contract adjustment. Herein, the medium- and long-term contracts represent the execution provisions for wind power, solar power, and hydropower within a set time threshold range. The optimal scheduling plan after contract adjustment is executed, and after the settlement period ends, the profit distribution scenario is reconstructed based on the actual operating data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the income of each member and complete the settlement. The members include wind power generators, photovoltaic power generators and hydropower storage entities.

[0008] Preferably, the step of converting and merging the lower-level optimization model into the upper-level optimization model to obtain a single-level MILP model, and inputting market data and hydropower unit status into the single-level MILP model to output the optimal scheduling plan, specifically involves: The constraints of the lower-level optimization model are extracted to the upper-level optimization model using the Kuhn-Takt conditions, forming a single-level mixed integer linear programming (MILP) model; wherein the constraints of the lower-level optimization model include wind power and photovoltaic power output constraints, reservoir water balance constraints, and pumped storage mutual exclusion constraints. Market data and hydropower unit status are input into a single-layer MILP model, and the Gurobi solver is used to solve the problem, outputting the optimal scheduling plan.

[0009] Preferably, the corresponding contract adjustment strategy is executed based on the potential execution deviation, specifically as follows: If the output of wind power and photovoltaic power is predicted to be insufficient, the reserved capacity of hydropower will be used to meet peak demand. If it is predicted that there will be an overcapacity in wind and solar power output, additional power transmission transactions will be reported in the inter-provincial spot market, or pumped storage will be arranged to absorb the excess power.

[0010] Preferably, after the settlement period ends, the profit distribution scenario is reconstructed based on the actual operating data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the profits of each member and complete the settlement. Specifically: Simulate two virtual scenarios: wind power and solar power operation only, and hydropower operation only, to obtain two theoretical benefits. and ; The actual total revenue of the new storage consortium is obtained by using the optimal output derived from a single-layer MILP model. To obtain the cooperative gain The specific expression is: ; ; Obtaining the deserved revenue from hydropower through synergistic gains Generate a settlement statement; the specific expression is: ; in, V Hydro The theoretical benefits are based solely on the hydropower operation scenario. This represents the theoretical returns under wind power and solar power operation scenarios only.

[0011] Preferably, when using cooperative game theory to obtain the benefits of each member and complete the settlement, it further includes: For a set with multiple member combinations Define the characteristic function The members are divided into wind power generators, photovoltaic power generators, and hydropower storage entities. Results for different combinations of members; Based on the corresponding member combination Optimal objective function value when participating in a single-layer MILP model Define the payoff function ; Obtain the Shapley values ​​of different members and get the allocated profits. The specific expression is: ; in, m As a member.

[0012] Preferably, the optimal scheduling plan is as follows: when a large photovoltaic power generation is predicted at noon and the electricity price in the province is low, an instruction is generated to start the pumped storage unit to pump water; when the electricity price rises during the evening peak, an instruction is generated to generate a discharge plan.

[0013] Preferably, the acquisition of forecast data and market data for wind power and photovoltaic power in the inter-provincial and intra-provincial two-tier electricity markets, as well as the unit status of hydropower, specifically involves: The system obtains forecast data and market data of wind power and photovoltaic power in the inter-provincial and intra-provincial two-level power markets, as well as the status of hydropower units, from the interfaces of SCADA system, meteorological stations and power trading platform. The status of hydropower units includes the current water level, dead water level and maximum water level of cascade reservoirs, as well as the start-up and shutdown status and ramp-up rate of pumped storage units.

[0014] This invention provides a two-tier optimization system for new energy storage consortia participating in a two-tier electricity market, comprising: The data acquisition module is used to acquire forecast data on wind power and photovoltaic power, market data, and hydropower unit status in the inter-provincial and intra-provincial two-tier electricity markets; wherein, the market data includes historical clearing prices, tie-line transmission capacity, and load forecast results of the inter-provincial and intra-provincial two-tier electricity markets; The model processing module is used to construct a lower-level optimization model with the goal of maximizing the net profit of the new energy storage consortium, and an upper-level optimization model with the goal of minimizing the cost of electricity purchase and the risk cost of conditional value of risk (CVaR). The lower-level optimization model is transformed and merged into the upper-level optimization model to obtain a single-level MILP model. Market data and the unit status of hydropower are input into the single-level MILP model, and the optimal scheduling plan is output. The new energy storage consortium refers to an energy system that coordinates the scheduling of wind power, photovoltaic power and hydropower. The identification module is used to identify potential execution deviations in medium- and long-term contracts by using wind power and photovoltaic power prediction data before the optimal scheduling plan is run, and to execute corresponding contract adjustment strategies based on the potential execution deviations to obtain the optimal scheduling plan after contract adjustment; wherein, the medium- and long-term contracts represent the execution provisions of wind power, photovoltaic power and hydropower within a set time threshold range; The settlement module is used to execute the optimal scheduling plan after contract adjustment, and after the settlement period ends, it reconstructs the profit distribution scenario based on the actual operation data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the income of each member and complete the settlement. The members include wind power generators, photovoltaic power generators and hydropower storage entities.

[0015] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market.

[0016] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market.

[0017] Compared with the prior art, the present invention has the following significant advantages: This invention constructs a lower-level optimization model and an upper-level optimization model for new energy storage consortia, as well as a single-level MILP model formed by converting and merging the lower-level optimization model into the upper-level optimization model. Market data and hydropower unit status are input into the single-level MILP model, which outputs the optimal dispatch plan. A CVaR risk management mechanism is introduced, enabling operational strategies in the inter-provincial and intra-provincial power markets to effectively avoid the huge loss risk caused by extreme electricity price fluctuations while pursuing high returns. It can provide a smoother and more controllable output curve, thereby obtaining higher credit lines and better transaction prices in the inter-provincial and intra-provincial power markets. Furthermore, by formulating contract adjustment strategies and reconstructing the profit distribution of scenarios, the economic value of regulation capacity is quantified, effectively resolving the interest disputes among new energy storage consortia, achieving a balance between returns and risks, and minimizing the loss caused by transaction prices. Attached Figure Description

[0018] Figure 1 The unit optimization scheduling diagram provided by this invention; Figure 2 The energy delay characteristic verification diagram provided for this invention; Figure 3 The present invention provides a power balance configuration diagram for a provincial system; wherein... Figure 3 (a) represents the power balance structure of the provincial system. Figure 3 (b) represents the clearing price for inter-provincial electricity trading; Figure 4 The flowchart of a new two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market is provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] How to leverage the inter-provincial and intra-provincial two-tier market structure, utilize the synergistic advantages of new energy and energy storage consortia to solve the problem of absorbing renewable energy during periods of high renewable energy generation in Northwest China, while balancing market economy and security, is a technical problem that urgently needs to be solved in the field of power market operation technology.

[0021] like Figure 4 As shown in this embodiment, a two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market includes: Step S1: Obtain forecast data, market data, and hydropower unit status for wind power and photovoltaic power in the inter-provincial and intra-provincial two-tier electricity markets.

[0022] The following data are obtained from the SCADA system, meteorological station, and power trading platform interfaces: Forecast data includes wind power and photovoltaic power forecast curves for the next 24 to 72 hours, with a forecast accuracy of 15 minutes; Market data includes: inter-provincial tie line transmission capacity (ATC), intra-provincial load forecast, and historical clearing prices in inter-provincial and intra-provincial markets; Hydropower unit status includes: current water level, dead water level, and maximum storage level of cascade reservoirs, as well as the start-up and shutdown status and ramp-up rate of pumped storage units.

[0023] Step S2: Construct a lower-level optimization model with the goal of maximizing the net profit of the new energy storage consortium, and construct an upper-level optimization model with the goal of minimizing the cost of electricity purchase and the risk cost of conditional value of risk (CVaR). Transform and merge the lower-level optimization model into the upper-level optimization model to obtain a single-level MILP model. Input market data and the unit status of hydropower into the single-level MILP model and output the optimal scheduling plan. Here, the new energy storage consortium represents an energy system that coordinates the scheduling of wind power, photovoltaic power and hydropower.

[0024] The specific steps for running the two-layer optimization algorithm proposed in this invention are as follows: Initialization: Set the risk preference coefficient. (e.g., 0.5) and confidence level (e.g., 0.95). Lower-level model construction: A mathematical model is established with the goal of maximizing the net profit of the new energy storage consortium, including constraints on wind and solar power output, reservoir water balance, and pumped storage mutual exclusion. Upper-level model construction: With the goal of minimizing system electricity purchase cost and CVaR risk cost, the inter-provincial transaction curve is used as the decision variable. Model transformation and solution: The constraints of the lower-level optimization model are extracted to the upper-level optimization model using Kuhn-Takt conditions, forming a single-level mixed integer linear programming (MILP) model. Market data and hydropower unit status are input into the single-level MILP model, and the Gurobi solver is used to solve the problem, outputting the optimal scheduling plan. Specific strategy example: When a midday solar power surge is predicted and the provincial electricity price is low, the algorithm automatically generates instructions to start pumped storage units; when the evening peak electricity price rises, instructions are generated to generate a discharge plan. By introducing cross-period regulation of pumped storage, the wind and solar curtailment rate can be reduced by 15%-30%.

[0025] This invention employs a two-layer planning architecture: Upper layer (inter-provincial transaction layer): As an agent, it optimizes the inter-provincial power purchase / sale curve based on the supply and demand gap and price difference within the province, with the goal of minimizing power purchase costs and risks.

[0026] Lower layer (new energy storage consortium physical layer): The new energy storage consortium responds to instructions from the upper layer, optimizing its internal pumping / generation / curtailment strategies, with the goal of maximizing the consortium's net profit.

[0027] New Reserve Consortium Physical Constraint Model: (1) Coupling constraints of the net on-grid power of the new energy storage consortium: The new energy storage consortium appears as a single node to the outside world, and its net output is formed by the aggregation of its subsystems: ; In the formula: For the new reserve consortium in Net internet power at any given time; This refers to the pumped storage discharge power; This refers to the pumping power of the pumped storage system.

[0028] (2) Pumped storage (energy storage) modeling: For sets For pumped storage units, establish cross-period energy balance and operational constraints: ① Storage capacity (state of charge) balance constraint: ; In the formula: For energy storage exist Energy state at any given moment (corresponding to reservoir water storage). The flow rate is the natural inflow interval. , These are pumping efficiency and power generation efficiency, respectively.

[0029] ② Mutual exclusion constraints of operating states: Introduce 0-1 state variables and : ; ③ Storage capacity upper and lower limits constraints: ; ④ Initial and final storage capacity balance constraints: ; (3) Constraints on new energy output ; In the formula, To predict available power, wind and solar power curtailment is permitted (curtailed power can be used for pumped storage and hydropower).

[0030] Inter-provincial transaction modeling (upper-layer core): Inter-provincial link transmission modeling: Inter-provincial transactions are abstracted as a set of connections. Power exchange. The net power received between provinces is defined as... .

[0031] Inter-provincial transaction power definition: .

[0032] ① Transmission capacity constraints of tie lines (considering safety margins): ; ② Inter-provincial transaction ramp-up constraints (considering the adjustment rate limits of inter-provincial agreements): ; (2) Intra-provincial system power balance constraints (including inter-provincial transactions): ; In the formula: To meet the load demand within the province; Indicates inter-provincial income (purchasing electricity), This indicates inter-provincial power transmission (sale of electricity).

[0033] (3) Considering the objective function of CVaR, the upper-level objective is to minimize the total system cost: ; In the formula: Forecast electricity prices for inter-provincial markets; It is a conditional risk value used to hedge against the risk of inter-provincial electricity price fluctuations.

[0034] Step S3: Before the optimal scheduling plan is run, potential execution deviations in medium- and long-term contracts are identified by using the predicted data of wind power and photovoltaic power, and corresponding contract adjustment strategies are implemented based on the potential execution deviations to obtain the optimal scheduling plan after contract adjustment; where medium- and long-term contracts refer to the execution provisions of wind power, photovoltaic power and hydropower within a set time threshold range.

[0035] In response to potential deviations during the execution of medium- and long-term contracts (such as sudden changes in wind and solar power output), this module initiates a contract adjustment mechanism based on the updated wind and solar power forecast data on D-2 day (two days before operation): if the forecast of insufficient wind and solar power output is met, the reserved capacity of hydropower will be used to boost peak power output and avoid high deviation assessment fees; if the forecast of excessive wind and solar power output is met, attempts will be made to increase the reported external transmission transactions in the inter-provincial spot market or arrange pumped storage for energy consumption.

[0036] Step S4: Execute the optimal scheduling plan after contract adjustment, and after the settlement period ends, reconstruct the profit distribution scenario based on the actual operation data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, use cooperative game theory to obtain the income of each member and complete the settlement; the members include wind power generators, photovoltaic power generators and hydropower storage entities.

[0037] After the transaction settlement cycle (e.g., daily / monthly) ends, this module reconstructs the profit distribution scenario based on actual operating data: it uses historical data to backtrack and simulate two virtual scenarios, "wind and solar power operation only" and "hydropower operation only," to calculate their theoretical returns. and Gain Calculation: Calculate the actual total return of the new storage consortium based on the optimal objective function value (output) obtained from the single-layer MILP model. The cooperative gain was obtained. The specific expression is: Financial Settlement: Obtaining the rightful revenue from hydropower through synergistic gains. Generate a settlement statement; the specific expression is: ;in, V Hydro The theoretical benefits are based solely on the hydropower operation scenario. This represents the theoretical returns under wind power and solar power operation scenarios only.

[0038] The marginal contribution of each member of the new reservoir consortium is calculated based on the Shapley value method.

[0039] For a set with multiple member combinations Define the characteristic function The members are divided into wind power generators, photovoltaic power generators, and hydropower storage entities. The results are categorized into two game players: "Regulatory Entity (Water Storage)" and "Fluctuating Entity (Wind Power, Solar Power)." This is based on the corresponding member combinations. Optimal objective function value when participating in a single-layer MILP model Define the payoff function .

[0040] Get different members The Shapley value (for example, cascade hydropower) and the profits allocated to it. for: .

[0041] In practice, three scenarios need to be calculated: Joint operation scenario: All units participate, and the results are... .

[0042] Water storage standalone operation scenario: forced Optimization yielded .

[0043] Wind power and solar power operating independently: mandatory Optimization yielded .

[0044] The new two-tier market collaborative operation system and method for energy storage consortia, which considers spatiotemporal coupling and risk avoidance, proposed in this invention has achieved the following significant technical and economic benefits through simulation examples: 1. Significantly improved the system's operational economy and risk resistance. Simulation results show that the total cost of collaborative operation of the new energy storage consortium (88,500 yuan) is far lower than the total cost of individual operation by each entity. Table 1 illustrates the various operating modes.

[0045] Comparative analysis: In standalone operation mode, wind power and photovoltaic groups face huge deviation penalty costs (approximately 10 million yuan in the simulation) due to large output fluctuations and inability to meet rigid load demands or inter-provincial transaction curves; while the standalone operation cost of cascade hydropower is approximately 96,200 yuan.

[0046] Synergistic Gains: After implementing the joint operation of this invention, the system achieved a synergistic gain (cost savings) of approximately 10 million yuan. This indicates that the invention effectively avoids the performance risks of wind power and photovoltaic units in the two-tier market through the regulation of hydropower and pumped storage, transforming the originally huge fines into the overall profit of the new energy storage consortium.

[0047] Table 1. Description of various operating modes It has enabled efficient cross-time-period consumption and value enhancement of wind and solar power resources.

[0048] like Figure 1 As shown, combining the unit optimization scheduling diagram and the cascade reservoir capacity analysis, it can be seen that: the physical characterization of energy time shift is as follows: during periods when inter-provincial electricity trading prices are low or the predicted output of wind power and photovoltaic power is greater than the external transmission curve (such as 0:00-5:00 AM), the scheduling results show that the pumped storage units are in pumping mode (power is negative), while the reservoir water level (capacity) curve shows an upward trend, which intuitively reflects the conversion and storage process of low-value electrical energy into potential energy; among them, the energy time delay characteristic verification diagram is as follows. Figure 2 As shown.

[0049] Market manifestations of value transfer: During periods of high inter-provincial electricity prices or insufficient wind and solar power output, dispatch results show a significant increase in hydropower output accompanied by a rapid decline in reservoir capacity curves. This indicates that previously stored energy has been precisely 'transferred' to high-value periods for release.

[0050] Conclusion: This 'charge-discharge' cycle trajectory, which is highly coupled with market price signals, proves that the present invention not only physically solves the absorption problem, but also economically captures the peak-valley price difference dividend by utilizing the 'spatiotemporal translation' characteristic, thereby maximizing the overall benefits of the new storage consortium.

[0051] A fair and reasonable benefit distribution mechanism has been established to promote multi-stakeholder cooperation. The profit distribution results based on Shapley values ​​demonstrate that this invention can quantify the marginal contribution of each entity to the alliance: Mutually Beneficial and Win-Win: The allocation results show that the cost-sharing (or net benefit) of cascade hydropower and wind power / photovoltaic groups under joint operation is better than that of operating alone. Specifically, cascade hydropower received an allocation benefit of approximately 50 million yuan (compared to operating alone), while wind power and photovoltaic groups also eliminated huge losses from operating alone through joint cost-sharing.

[0052] Quantifying the value of regulation: This mechanism fairly recognizes the economic value of the regulation capacity (pumping / discharging) provided by cascade hydropower, while also recognizing the capacity value of wind power and solar power providing cheap and clean electricity. It provides an enforceable settlement basis for breaking down the barriers between wind power, solar power and hydropower, which belong to different stakeholders.

[0053] Power balance diagram of the provincial system (see appendix) Figure 3 The data shows that the net output of the new energy storage consortium (green area) and inter-provincial power purchases (purple area) complement each other well. When system load demand is high, the new energy storage consortium maximizes output through internal coordination, reducing reliance on expensive inter-provincial spot power or high-emission thermal power. The smoothness of the new energy storage consortium's output is significantly better than that of wind power or photovoltaic power alone, reducing the grid's demand for reserve capacity and improving the overall stability of the power system.

[0054] This invention proposes a two-tier optimization system for new energy storage consortia participating in a two-tier electricity market, comprising: The data acquisition module is used to acquire forecast data on wind power and photovoltaic power, market data, and hydropower unit status in the inter-provincial and intra-provincial electricity markets. Market data includes historical clearing prices, tie-line transmission capacity, and load forecasts for both inter-provincial and intra-provincial electricity markets. The model processing module is used to construct a lower-level optimization model aiming to maximize the net profit of the new energy storage consortium, and an upper-level optimization model aiming to minimize the cost of electricity purchase and the risk cost of conditional value of risk (CVaR). The lower-level optimization model is transformed and merged into the upper-level optimization model to obtain a single-level MILP model. Market data and hydropower unit status are input into the single-level MILP model, and the optimal dispatch plan is output. Here, the new energy storage consortium represents wind power and photovoltaic power. An energy system that coordinates with hydropower scheduling; an identification module is used to identify potential execution deviations in medium- and long-term contracts by using predicted data of wind and solar power before the optimal scheduling plan is implemented, and to execute corresponding contract adjustment strategies based on the potential execution deviations to obtain the optimal scheduling plan after contract adjustment; where medium- and long-term contracts represent the execution provisions of wind, solar and hydropower within a set time threshold range; a settlement module is used to execute the optimal scheduling plan after contract adjustment, and after the settlement period ends, to reconstruct the profit distribution scenario based on the actual operating data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the income of each member and complete the settlement; the members include wind power generators, solar power generators and hydropower storage entities.

[0055] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market.

[0056] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0057] The present invention also provides a storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of a two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market.

[0058] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0059] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A two-tier optimization method for new energy storage consortia participating in a two-tier electricity market, characterized in that, include: Acquire forecast data on wind power and photovoltaic power output, market data, and hydropower unit status in the inter-provincial and intra-provincial two-tier electricity markets; wherein, the market data includes historical clearing prices, tie-line transmission capacity, and load forecast results of the inter-provincial and intra-provincial electricity markets; A lower-level optimization model is constructed with the goal of maximizing the net profit of the new energy storage consortium, and an upper-level optimization model is constructed with the goal of minimizing the cost of electricity purchase and the risk cost of conditional value of risk (CVaR). The lower-level optimization model is transformed and merged into the upper-level optimization model to obtain a single-level MILP model. Market data and the unit status of hydropower are input into the single-level MILP model to output the optimal scheduling plan. The new energy storage consortium refers to an energy system that coordinates the scheduling of wind power, photovoltaic power and hydropower. Before the optimal scheduling plan is implemented, potential execution deviations in medium- and long-term contracts are identified using wind power and solar power forecast data. Corresponding contract adjustment strategies are then implemented based on these potential execution deviations to obtain the optimal scheduling plan after contract adjustment. Herein, the medium- and long-term contracts represent the execution provisions for wind power, solar power, and hydropower within a set time threshold range. The optimal scheduling plan after contract adjustment is executed, and after the settlement period ends, the profit distribution scenario is reconstructed based on the actual operating data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the income of each member and complete the settlement. The members include wind power generators, photovoltaic power generators and hydropower storage entities.

2. The two-tier optimization method for a new energy storage consortium participating in a two-tier electricity market as described in claim 1, characterized in that, The process involves transforming and merging the lower-level optimization model into the upper-level optimization model to obtain a single-level MILP model. Market data and the unit status of hydropower are then input into the single-level MILP model to output the optimal scheduling plan. Specifically: The constraints of the lower-level optimization model are extracted to the upper-level optimization model using the Kuhn-Takt conditions, forming a single-level mixed integer linear programming (MILP) model; wherein the constraints of the lower-level optimization model include wind power and photovoltaic power output constraints, reservoir water balance constraints, and pumped storage mutual exclusion constraints. Market data and hydropower unit status are input into a single-layer MILP model, and the Gurobi solver is used to solve the problem, outputting the optimal scheduling plan.

3. The two-tier optimization method for a new energy storage consortium participating in a two-tier electricity market as described in claim 1, characterized in that, The corresponding contract adjustment strategy is executed based on the potential execution deviation, specifically as follows: If the output of wind power and photovoltaic power is predicted to be insufficient, the reserved capacity of hydropower will be used to meet peak demand. If it is predicted that there will be an overcapacity in wind and solar power output, additional power transmission transactions will be reported in the inter-provincial spot market, or pumped storage will be arranged to absorb the excess power.

4. The two-tier optimization method for a new energy storage consortium participating in a two-tier electricity market as described in claim 1, characterized in that, After the settlement period ends, the profit distribution scenario is reconstructed based on the actual operating data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the revenue of each member and complete the settlement, specifically as follows: Simulate two virtual scenarios: wind power and solar power operation only, and hydropower operation only, to obtain two theoretical benefits. and ; The actual total revenue of the new storage consortium is obtained by using the optimal output derived from a single-layer MILP model. To obtain the cooperative gain The specific expression is: ; ； Obtaining the deserved revenue from hydropower through synergistic gains Generate a settlement statement; the specific expression is: ； in, V Hydro The theoretical benefits are based solely on the hydropower operation scenario. This represents the theoretical returns under wind power and solar power operation scenarios only.

5. A two-tier optimization method for a new energy storage consortium participating in a two-tier electricity market as described in claim 1, characterized in that, The process of obtaining the benefits of each member and completing the settlement through cooperative game theory also includes: For a set with multiple member combinations Define the characteristic function The members are divided into wind power generators, photovoltaic power generators, and hydropower storage entities. Results for different combinations of members; Based on the corresponding member combination Optimal objective function value when participating in a single-layer MILP model Define the payoff function ; Obtain the Shapley values ​​of different members and get the allocated profits. The specific expression is: ； in, m As a member.

6. The two-tier optimization method for a new energy storage consortium participating in a two-tier electricity market as described in claim 1, characterized in that, The optimal scheduling plan is as follows: when a large photovoltaic power generation is predicted at noon and the electricity price in the province is low, an instruction is generated to start the pumped storage unit to pump water; when the electricity price rises during the evening peak, an instruction is generated to generate a discharge plan.

7. A two-tier optimization method for new energy storage consortia participating in a two-tier electricity market as described in claim 1, characterized in that, The acquisition of forecast data and market data for wind power and photovoltaic power in the inter-provincial and intra-provincial two-tier electricity markets, as well as the unit status of hydropower, specifically involves: The system obtains forecast data and market data of wind power and photovoltaic power in the inter-provincial and intra-provincial two-level power markets, as well as the status of hydropower units, from the interfaces of SCADA system, meteorological stations and power trading platform. The status of hydropower units includes the current water level, dead water level and maximum water level of cascade reservoirs, as well as the start-up and shutdown status and ramp-up rate of pumped storage units.

8. A two-tier optimization system for a new energy storage consortium participating in a two-tier electricity market, characterized in that, include: The data acquisition module is used to acquire forecast data on wind power and photovoltaic power, market data, and hydropower unit status in the inter-provincial and intra-provincial two-tier electricity markets; wherein, the market data includes historical clearing prices, tie-line transmission capacity, and load forecast results of the inter-provincial and intra-provincial two-tier electricity markets; The model processing module is used to construct a lower-level optimization model with the goal of maximizing the net profit of the new energy storage consortium, and an upper-level optimization model with the goal of minimizing the cost of electricity purchase and the risk cost of conditional value of risk (CVaR). The lower-level optimization model is transformed and merged into the upper-level optimization model to obtain a single-level MILP model. Market data and the unit status of hydropower are input into the single-level MILP model, and the optimal scheduling plan is output. The new energy storage consortium refers to an energy system that coordinates the scheduling of wind power, photovoltaic power and hydropower. The identification module is used to identify potential execution deviations in medium- and long-term contracts by using wind power and photovoltaic power prediction data before the optimal scheduling plan is run, and to execute corresponding contract adjustment strategies based on the potential execution deviations to obtain the optimal scheduling plan after contract adjustment; wherein, the medium- and long-term contracts represent the execution provisions of wind power, photovoltaic power and hydropower within a set time threshold range; The settlement module is used to execute the optimal scheduling plan after contract adjustment, and after the settlement period ends, it reconstructs the profit distribution scenario based on the actual operation data of the optimal scheduling plan after contract adjustment. In the reconstructed profit distribution scenario, cooperative game theory is used to obtain the income of each member and complete the settlement. The members include wind power generators, photovoltaic power generators and hydropower storage entities.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of a two-tier optimization method for a new energy storage consortium participating in a two-tier electricity market as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the two-tier optimization method for a new energy storage consortium to participate in a two-tier electricity market as described in any one of claims 1 to 7.

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