A double-layer day-ahead spot market clearing method for new energy consumption

Abstract

The application belongs to the technical field of power system and its automation, and relates to a double-layer day-ahead spot market clearing method for new energy consumption, comprising: 1. new energy output uncertainty quantification and scene reduction error modeling based on probability weight; 2. construction of full electricity quantity "quantity reporting and price reporting" mechanism and monotonicity declaration constraint; 3. day-ahead pre-clearing in the province; 4. inter-provincial centralized clearing; 5. formal clearing in the province; the application effectively solves the core problems in the prior art, such as new energy consumption limitation, price discovery obstruction, weak resource overall planning capability and missing theoretical framework, and achieves significant improvement in aspects of improving new energy utilization efficiency, optimizing power market price mechanism, guaranteeing system dispatching reliability and timeliness and the like.

Description

Technical Field

[0001] This invention belongs to the field of power system and automation technology, specifically relating to a two-tier day-ahead spot market clearing method for renewable energy consumption. Background Technology

[0002] With the rapid development of the new energy industry, the penetration rate of new energy in the power system continues to increase, and its participation mechanism in the electricity market is also constantly evolving and improving. In the early stages of the development of the new energy market participation mechanism, two core incentive policies were mainly relied upon to ensure the grid connection and consumption of new energy: one was the Renewable Energy Quota System (RPS) based on electricity volume, which forced market participants to consume new energy by setting quota requirements; the other was price-based incentive policies, including Feed-in Tariff (FIT) and Contracts for Difference (CFD), which provided stable revenue guarantees for new energy power generation companies and reduced their investment and operational risks. Currently, with the gradual establishment and maturation of the electricity spot market, the models for new energy participation in the spot market have diversified, mainly forming three mainstream models: first, the volume-based, no-bid model, where new energy power generation is given priority in market clearing as a market boundary condition, without needing to participate in market bidding; second, the partial bidding model, where a portion of new energy power generation participates in market bidding, and the remaining power generation is cleared according to fixed rules; and third, the full-volume bidding model, where all new energy power generation is included in the market bidding system and competes fairly with other power sources for clearing.

[0003] In terms of macro-level electricity market architecture, relatively mature mainstream models have emerged internationally, with the US PJM market and the European market being the most representative. Under the US PJM market framework, the Safety-Constrained Unit Combination (SCUC) and Safety-Constrained Economic Dispatch (SCED) models are widely adopted to achieve optimized dispatch and efficient allocation of electricity resources. The European market, based on the Regional Price Coupling (PCR) mechanism, employs Zoned Marginal Pricing (ZMP) to effectively guide centralized bidding and unified clearing of cross-border electricity resources, improving the efficiency of coordinated utilization of electricity resources within the region. In contrast, traditional inter-provincial electricity trading in my country mostly follows a "high-low matching" clearing model based on a single interconnection line. This model is intuitive to operate but struggles to adapt to the resource optimization needs under the background of high-proportion renewable energy grid integration.

[0004] Despite the continuous development of mechanisms for renewable energy participation in the electricity market, existing technologies still have many objective shortcomings in practical applications, which seriously restrict the efficient consumption of renewable energy and the healthy development of the electricity market. These shortcomings are as follows: First, the scope of absorption is limited. The geographical distribution of new energy resources and electricity load demand in my country is extremely uneven. New energy resources are mainly concentrated in the Northwest and North China, while electricity load demand is mainly concentrated in the economically developed eastern and southern regions. Relying solely on electricity trading within a single province is insufficient to overcome geographical limitations and cannot completely solve the absorption bottleneck caused by the high proportion of new energy grid connection, resulting in the curtailment of some new energy power generation.

[0005] Secondly, it hinders price discovery. In the traditional model, renewable energy is often treated as a priority clearing under fixed boundary conditions and does not participate in bidding. While this approach ensures priority consumption of renewable energy to some extent, it also prevents renewable energy from reflecting its true power generation cost and environmental value through market bidding. This hinders the effective functioning of the electricity spot market price discovery mechanism and is also detrimental to the formation of a fair competitive market environment for various power sources.

[0006] Secondly, the ability to coordinate resources is weak. my country's traditional inter-provincial power trading adopts a "high-low matching" clearing model based on a single tie line, which only focuses on the power balance of a single tie line and lacks a global consideration of the power resources of the entire network. This limits the overall optimization and allocation of power resources across regions and provinces, fails to give full play to the resource endowment advantages of different regions, and reduces the overall operating efficiency of the power system.

[0007] Finally, a theoretical framework is lacking. Current research largely treats the modeling of new energy uncertainty and the electricity market clearing problem in isolation, lacking systematic integration and connection. In particular, under the new energy full-scale bidding mechanism, how to accurately quantify the uncertainty of new energy power generation, and how to achieve two-level price coupling and linkage between intra-provincial nodal marginal price (LMP) and inter-provincial zone marginal price (ZMP), lacks a complete theoretical framework to support this cross-disciplinary field. This makes it difficult for the existing market mechanism to adapt to the development needs of new energy full-scale bidding, and fails to effectively solve the market dispatch and resource allocation problems brought about by high proportion of new energy grid connection.

[0008] In summary, existing technologies and mechanisms for renewable energy participation in the electricity market suffer from problems such as limited absorption scope, hindered price discovery, weak resource coordination capabilities, and a lack of theoretical framework. A new technological solution is urgently needed to address these shortcomings and promote the efficient absorption of renewable energy and the high-quality development of the electricity market. Summary of the Invention

[0009] This invention provides the following technical solution: a two-tier day-ahead spot market clearing method for renewable energy consumption, comprising the following steps: Step S1. Quantification of uncertainty in new energy output and error modeling of scenario reduction based on probability weights: For the error in the new energy day-ahead market declaration forecast curve caused by the strong randomness of the next day's weather conditions, it is assumed that the forecast error follows a normal distribution, and an initial output scenario set is generated based on this distribution.

[0010] Step S2. Construct a "reporting and pricing" mechanism for the total electricity volume and a monotonic reporting constraint, stipulating that thermal power units, new energy units, and load sides should all adopt the "reporting and pricing" model to participate in the total electricity volume market competition.

[0011] Step S3. Provincial pre-clearance: Based on local unit bids and load forecasts, dispatching agencies in each province, using regional tie line power as a preset constraint boundary, conduct safety constraint unit combination and economic dispatch calculations based on the AC lossless network constraint model.

[0012] Step S4. Centralized Clearing Between Provinces: The inter-provincial trading center receives the electricity surplus / deficit reported by each province and performs unified optimization and clearing.

[0013] Step S5. Formal Clearance within the Province: Each province receives the tie-line adjustment plan, updates it to the system security-related constraints, and replaces the original tie-line pre-planned power.

[0014] Preferably, step S1 includes the following sub-steps: S1-1. Scene Dimensionality Reduction: The K-means clustering algorithm is used to reduce the massive initial scenes into multiple typical representative scenes, and the occurrence probability weight corresponding to each representative scene is calculated.

[0015] S1-2. Probability Internalization: In the subsequent unified clearing calculation across multiple scenarios, each typical scenario and its probability weight are directly incorporated into the objective function of the market operator to drive the maximization of the expected value of social welfare across the entire network.

[0016] Preferably, step S2 includes the following sub-steps: S2-1. Data Structure: Thermal power units and load side adopt ordinary hourly segmented pricing, submitting aggregated data containing "price-electricity-time period" to clarify the correspondence between instantaneous electrical energy and settlement unit price within a specific time period; new energy units currently adopt a daily fixed pricing strategy.

[0017] S2-2. Monotonicity constraint: The electricity price quotation curve of the generator set must follow the principle of monotony and non-decreasing; the electricity declaration curve on the user side must meet the principle of monotony and non-increasing.

[0018] Preferably, step S3 includes the following sub-steps: S3-1. Establishment of the objective function: Establish a social welfare maximization objective function based on mixed integer linear programming, which comprehensively considers the probability weighted results under multiple scenarios.

[0019] S3-2. Variable decoupling settings: The winning variables for power generation and load are accompanied by scenario index dimensions; the start-up and shutdown status of the units are set as variables without scenario factors.

[0020] S3-3. Physical system constraints: The model solution must satisfy the system power generation load balance constraint, the line power flow constraint based on the upper and lower limits of transmission capacity, and the upper and lower limits of section capacity application and output of thermal power units; when considering system safety-related constraints, the power of the regional tie line is used as the solution boundary within the province.

[0021] S3-4. Electricity Price and Boundary Output: The initial nodal marginal electricity price, which accurately reflects the supply and demand relationship of the nodes, is calculated based on the power transfer distribution factor. Subsequently, the results of unit start-up and shutdown plans, the unabsorbed power generation surplus within the province, and the load deficit are extracted and passed to the inter-provincial day-ahead market model as boundary conditions.

[0022] More preferably, in step S3-1, the objective function for maximizing social welfare is:

[0023] In the formula: Let S represent the output probability of each new energy scenario, and let S represent the set of all scenarios. express In this scenario, the electricity purchase cost for electricity buyer u at time t is given, where U represents the sum of costs paid by electricity retailers and wholesale users participating in the provincial day-ahead market, and T represents the sum of time periods. Let N represent the unit startup cost of thermal power unit i at time t in the given scenario, and let N represent the total number of thermal power units participating in the provincial day-ahead market. express The operating cost of thermal power unit i at time t in the given scenario; express The operating cost of new energy unit i at time t in the scenario, where NE represents the total number of new energy units.

[0024] In step S3-3, the system generation load balance constraints and line power flow constraints are as follows:

[0025] In the formula: when conducting pre-clearance within the province, The planned power (in MW) of regional link line j at time t; when the formal clearing within the province is carried out. Let the adjusted power (in MW) of the regional tie line j at time t be ; , This indicates the upper and lower limits of the transmission capacity of AC line l in MW. express The power (in MW) of thermal power unit i at time t in the given scenario. express The power (in MW) of the new energy unit i at time t in the given scenario. express In this scenario, the electricity purchased by the electricity purchaser u at time t is expressed as an amount in MW. , , These represent the set of thermal power units, the set of new energy power units, and the set of nodes, respectively.

[0026] Preferably, step S4 includes the following sub-steps: S4-1 Objective function considering network losses and transmission costs: With the goal of maximizing social welfare, the objective function additionally considers the total transmission cost calculated based on the transfer distribution factor, and realizes the implicit auction of transmission rights by solving the optimization model; at the same time, line losses are considered in the system load balance constraints.

[0027] S4-2 AC / DC hybrid power grid ATC constraints: Apply available transmission capacity constraints to prevent congestion: For DC tie lines: constrain the sensitivity distribution factor of the DC tie line based on the node, limiting it to the upper and lower limits of the available DC transmission capacity; For AC tie lines: constrain the sensitivity distribution factor of the AC tie line based on the node, limiting it to the upper and lower limits of the maximum forward / reverse available transmission capacity.

[0028] S4-3 Settlement Mechanism and Instruction Issuance: A regional electricity price model is adopted, which treats a single province as an equivalent single node to obtain regional electricity price after clearing; after clearing is completed, the inter-provincial clearing and connection line adjustment plan is issued to each province.

[0029] More preferably, in step S4-1, the objective function that takes into account network losses and transmission costs is:

[0030] In the formula, Let S represent the output probability of each new energy scenario, and let S represent the set of all scenarios. express In this scenario, the electricity purchase cost for electricity buyer u at time t is given, where U represents the sum of electricity sales companies and wholesale users participating in the provincial day-ahead market, T represents the sum of time intervals, and N represents the total number of thermal power units participating in the provincial day-ahead market. express The operating cost of thermal power unit i at time t in the given scenario; express The operating cost of renewable energy unit i at time t in the given scenario, where NE represents the total number of renewable energy units. express The total transmission cost of the system in this scenario; the system load balancing constraint taking into account line losses is:

[0031] In the formula: for In this scenario, the line loss (in MW) of the tie line h at time t; Let be the dual multiplier corresponding to the system's power generation load balance constraint in time period t.

[0032] In step S4-2, the power flow of the DC tie line satisfies:

[0033] In the formula: Let represent the sensitivity distribution factor of node i to DC tie line d. , These represent the upper and lower limits of the available transmission capacity of the DC tie line d, respectively.

[0034] Preferably, step S5 includes the following sub-steps: S5-1 Fixed unit start-up and shutdown status, that is, the 0-1 state variables calculated in step S3 are used as constant inputs; S5-2 restarts formal economic dispatch clearing calculations to determine the final winning unit output and calculate the final convergent nodal price across the entire network. This mechanism preserves the autonomy of each provincial dispatch center and forms a loosely coupled architecture of "boundary parameter transfer".

[0035] The beneficial effects of this invention are: 1. This invention breaks through physical barriers to achieve wide-area and deep absorption of new energy, effectively reducing the curtailment rate. The loosely coupled two-layer architecture constructed in this invention fundamentally overcomes the shortcomings of a single provincial pre-clearing mechanism limited by local load capacity and transmission corridor carrying capacity, breaking down resource barriers between regions. Through an inter-provincial centralized clearing mechanism that considers the available transmission capacity (ATC) of the AC / DC hybrid grid, this invention can effectively promote the external transmission of surplus low-marginal-cost new energy power from new energy-rich areas, precisely alleviating local grid congestion at the physical level and improving the efficiency of cross-regional power resource flow. Simulation verification shows that the technical effect of this invention is significant, with a substantial reduction in the overall curtailment rate in new energy-rich areas, for example, from 21.9% to 18.6%, and a significant increase in cross-regional power transmission, effectively solving the absorption bottleneck caused by a high proportion of new energy grid connection and improving the utilization efficiency of new energy.

[0036] 2. This invention achieves efficient transmission of economic signals, smooths out extreme regional electricity price differences, and improves overall social welfare. Under the traditional single-province clearing model, the fragmentation of power resources between regions easily leads to extreme tiered electricity prices—new energy-rich areas become marginal units due to new energy generation, resulting in extremely low price troughs; while load-rich areas become marginal units due to high-priced thermal power, resulting in extremely high price peaks, causing regional price imbalances and hindering the healthy development of the electricity market. This invention introduces a two-layer price coupling mechanism, utilizing the regional marginal price (ZMP) calculated in the inter-provincial market to play a crucial "anchoring effect," effectively smoothing out unreasonable price differences between regions and guiding regional electricity prices towards a more reasonable level. This mechanism ultimately feeds back to the formal clearing process within the province, resulting in a significant convergence effect of the regional marginal price (LMP) across the entire network, effectively optimizing the allocation efficiency of power resources and achieving a Pareto improvement in overall social welfare. Verification shows that overall social welfare can be increased by 3.17%, achieving a dual improvement in economic benefits and resource allocation efficiency.

[0037] 3. This invention achieves robust multi-scenario control that balances uncertainty and system computing power, ensuring timely and reliable clearing. Traditional deterministic clearing models, due to insufficient consideration of prediction biases in renewable energy generation, are prone to a surge in system reserve costs and are difficult to adapt to the dispatching needs of large-scale power systems. This invention designs a probabilistic weighted objective function, where the 0-1 start-up and shutdown variables are independent of the scenario setting, while the continuous output variable depends on the scenario setting. This fully considers the robustness of full-scale bidding clearing of high-proportion renewable energy, effectively addresses the uncertainty of renewable energy generation, and reduces system reserve costs. Furthermore, through K-means clustering dimensionality reduction, it significantly reduces the solution dimensionality of the mixed integer linear programming (MILP) model, greatly improving the model's solution efficiency and ensuring the timeliness of day-ahead clearing in large-scale power systems. This achieves synergistic optimization of robustness and computing power efficiency, providing reliable technical support for high-proportion renewable energy to participate in the electricity market through full-scale bidding.

[0038] 4. This invention effectively solves the core problems in the prior art, such as limited new energy consumption, hindered price discovery, weak resource coordination capabilities, and lack of theoretical framework. It has achieved significant improvements in improving the efficiency of new energy utilization, optimizing the electricity market price mechanism, and ensuring the reliability and timeliness of system dispatch. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the two-tier day-ahead market model architecture of a two-tier day-ahead spot market clearing method for renewable energy consumption according to the present invention. Figure 2 This is a test diagram of the AC / DC hybrid system grid and region division according to the present invention; Figure 3This is a schematic diagram illustrating the generation, reduction, and probability distribution of new energy output scenarios according to the present invention. Figure 4 This is a schematic diagram illustrating the cross-provincial consumption benefit assessment (curtailment rate comparison and power flow dispatch) of the two-tier market of the present invention; Figure 5 This is a comparison chart showing the evolution and convergence of nodal price (LMP) and regional price (ZMP) in this invention; Figure 6 This is the overall code flowchart of the present invention. Detailed Implementation

[0040] The relevant technologies of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0041] like Figures 1-6 As shown in the figure, the specific steps of a two-tier day-ahead spot market clearing method for renewable energy consumption in this embodiment are as follows: Step S1: Quantification of uncertainty in new energy output and modeling of scenario reduction error based on probability weights: In view of the strong randomness of the next day's weather conditions, which leads to errors in the prediction curve of new energy in the day-ahead market, it is assumed that the prediction error follows a normal distribution, and a large-scale initial output scenario set is generated accordingly (e.g., 200 initial scenarios are generated).

[0042] The specific steps include: S1-1 Scenario Dimensionality Reduction: In order to balance the accuracy of the solution and the computational efficiency, the K-means clustering algorithm is used to efficiently reduce the massive initial scenarios to a number of typical representative scenarios (e.g., reduced to 3 typical scenarios), and the occurrence probability weight corresponding to each representative scenario is calculated.

[0043] S1-2 Probability Internalization: In the subsequent unified clearing calculation across multiple scenarios, each typical scenario and its probability weight are directly incorporated into the objective function of the market operator to drive the maximization of the expected value of social welfare across the entire network.

[0044] Step S2: Establish a full-power "reporting and pricing" mechanism and monotonic reporting constraints. Break the traditional "new energy priority consumption and no pricing" model, and stipulate that thermal power units, new energy units and load side shall all adopt the "reporting and pricing" model to participate in the full-power market competition.

[0045] The specific steps include: S2-1 Data Structure: Thermal power units and the load side adopt ordinary hourly block bids, submitting aggregated data including "price-electricity-time period" to clarify the correspondence between instantaneous electrical energy and settlement unit price within a specific time period. New energy units currently adopt a daily fixed bidding strategy.

[0046] S2-2 Monotonicity Constraint: To ensure the convexity of the market clearing model, the electricity price quotation curve of generating units (including thermal power and new energy) must follow the monotonically non-decreasing principle (i.e., increasing marginal cost); the electricity declaration curve of the user side (load) must strictly satisfy the monotonically non-increasing principle (i.e., decreasing marginal utility).

[0047] Step S3: Provincial pre-clearance (first level, safety-constrained unit combination and economic dispatch) The dispatching agencies of each province calculate SCUC (safety-constrained unit combination) and SCED (economic dispatch) based on the local unit bids and load forecasts, with the regional tie line power as the preset boundary, and based on the AC lossless network constraint model.

[0048] The specific steps include: S3-1 Objective Function Establishment: A social welfare maximization objective function based on mixed-integer linear programming (MILP) is established. This function comprehensively considers the probability-weighted results under multiple scenarios, specifically including the weighted summation of various costs: electricity purchase costs for the electricity buyer, operating and start-up costs of thermal power units, and operating costs of renewable energy units.

[0049] In the formula: Let S be the output probability of each new energy scenario, and S be the set of all scenarios. for In this scenario, the electricity purchase cost for electricity purchaser u at time t is in yuan, where U represents the sum of electricity sales companies and wholesale users participating in the day-ahead market within the province. In this scenario, the unit startup cost of thermal power unit i at time t is in yuan, and N represents the total number of thermal power units participating in the provincial day-ahead market. for Operating cost of thermal power unit i at time t in the given scenario (in yuan); for In this scenario, the operating cost of renewable energy unit i at time t is given, and NE represents the total number of renewable energy units. Regarding the variables in the model, it's important to clarify that the bidding for power generation and load can be divided into multiple scenarios, thus scenario factors are included in the variables. However, the cost and time involved in changing the start-up and shutdown combinations of units are significant and need to be confirmed in advance. Therefore, the start-up and shutdown combination results for all units are applicable to all scenarios, and scenario factors are not included in the variables.

[0050] S3-2 Variable Decoupling Settings: The winning variables for power generation and load are accompanied by scenario index dimensions (which change according to the scenario); while the start-up and shutdown status of the unit (0-1 variable) is set as a variable without scenario factors due to the high cost and long time consumption of changing it (i.e., a set of start-up and shutdown results are applicable to all scenarios).

[0051] S3-3 Physical System Constraints: The model solution must satisfy the system's power generation load balance constraints, line power flow constraints based on transmission capacity upper and lower limits, and section capacity application upper and lower limits and output upper and lower limits for thermal power units. When considering system safety-related constraints, the power of the regional tie line is used as the solution boundary within the province. The system's power generation load balance constraints and line power flow constraints are as follows:

[0052] In the formula: when conducting pre-clearance within the province, The planned power (in MW) of regional link line j at time t, when the formal clearing within the province is carried out. Let the adjusted power (in MW) of the regional tie line j at time t be ; , The upper and lower limits of the transmission capacity of AC line l are in MW.

[0053] Unit bid quantity constraints:

[0054] In the formula: , The upper and lower limits of the declared power in MW for thermal power unit i at time t in segment m; Let be the 0-1 state variable for the start-up and shutdown of thermal power unit i at time t. Its value is 1 when the unit is started and 0 when the unit is shut down. , The upper and lower limits of output (in MW) set for thermal power unit i at time t.

[0055] S3-4 Electricity Price and Boundary Output: The initial nodal marginal price (LMP) is calculated based on the power transfer distribution factor (PTDF) to accurately reflect the supply and demand relationship at each node. Subsequently, the results of unit start-up and shutdown plans, the unabsorbed generation surplus within the province (and its price curve), and the load deficit (and its price curve) are extracted and used as boundary conditions to propagate to the inter-provincial day-ahead market model. When discussing only the intra-provincial electricity market, the total number of nodes and lines in the system is relatively small, and congestion occurs less frequently. In this case, using the nodal price model can more accurately reflect the supply and demand relationship at each node. According to the definition of nodal price, the general formula for solving the nodal price is:

[0056] In the formula: Let $t be the electricity price at node k at time t in yuan·MWh⁻¹. The power transfer distribution factor is calculated for node k on line l.

[0057] Step S4: Centralized Clearing Between Provinces (Inter-provincial Coordination Mechanism) The inter-provincial trading center receives the surplus / deficit reported by each province and conducts unified optimization and clearing.

[0058] The specific steps include: S4-1 Objective Function Considering Network Losses and Transmission Costs: This level also aims to maximize social welfare, but it must additionally consider the total transmission cost calculated based on the transfer distribution factor in the objective function, achieving implicit auctioning of transmission rights by solving the optimization model. Simultaneously, line losses are considered in the system load balance constraints.

[0059] In the formula for The total power transmission cost of the system in this scenario is calculated using the following formula:

[0060] In the formula: Let h be the transition distribution factor of the connection line h to node i.

[0061] S4-2 AC / DC Hybrid Grid ATC Constraints: Applicable Transmission Capacity (ATC) constraints are applied to prevent congestion. For DC tie lines: their power flow is adjustable, constrained based on the node sensitivity distribution factor of the DC tie line (connected sending and receiving node sensitivities are 1 and -1 respectively, with the rest being 0), limited to the upper and lower limits of available DC transmission capacity. For AC tie lines: based on the node sensitivity distribution factor of the AC tie line, limited to the upper and lower limits of maximum forward / reverse available transmission capacity; the power flow of the DC tie line is adjustable, and its magnitude is related to generation, as shown below:

[0062] In the formula: The sensitivity distribution factor of node i to DC tie line d is 1 and 1 respectively for the sending and receiving nodes connected to DC tie line d, and the sensitivity distribution factors of the remaining nodes not connected to it are 0. , This represents the upper and lower limits of the available transmission capacity of DC tie line d in MW.

[0063] AC network ATC constraint reference: Common power flow constraints of transmission lines:

[0064] In the formula: Let be the sensitivity distribution factor of node i to AC tie line l, and let be the power of DC tie line d attached to the node it is connected to. , The upper and lower limits of the available transmission capacity of AC tie line l in MW; , The dual multiplier corresponding to the maximum forward / reverse available transmission capacity constraint of AC tie line l.

[0065] S4-3 Settlement Mechanism and Instruction Issuance: A regional pricing model is adopted, treating a single province as an equivalent single node to obtain a regional electricity price (ZMP) upon clearing. After clearing is completed, the inter-provincial clearing tie-line adjustment plan is issued to each province.

[0066] Step S5: Formal Clearing within the Province (Second Tier, Establishing the Final Bid and Price) Each province receives the adjusted connection line plan, updates it to the system security-related constraints, and replaces the original pre-planned power.

[0067] The specific steps include: S5-1 Fixed unit start-up and shutdown status (i.e., the 0-1 state variables calculated in S3 are used as constant inputs); S5-2 restarts formal economic dispatch (SCED) clearing calculations to determine the final winning unit output and calculate the final convergent nodal price (LMP) for the entire network. This mechanism preserves the autonomy of each provincial dispatch center, forming a loosely coupled architecture of "boundary parameter transfer".

[0068] In summary, this invention addresses the pain points of inefficient cross-provincial and cross-regional resource allocation, insufficient renewable energy consumption capacity, and poor robustness and low solution efficiency of existing electricity market clearing frameworks in scenarios with high proportions of renewable energy integration. It proposes a two-layer coordinated day-ahead spot market clearing architecture, which handles the uncertainty of renewable energy output through probabilistic scenarios, explores cross-provincial and cross-regional allocation capacity through a full-volume bidding mechanism, optimizes congestion management based on the LMP and ZMP two-layer price transmission mechanisms, promotes price convergence, effectively improves renewable energy consumption capacity, reduces system operating costs, and enhances overall social welfare. This provides a feasible technical solution for high-proportion renewable energy participation in the electricity market.

[0069] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A two-tier day-ahead spot market clearing method for renewable energy consumption, characterized in that, Includes the following steps: Step S1. Quantification of uncertainty in new energy output and error modeling of scenario reduction based on probability weights: For the error in the forecast curve of new energy day-ahead market declaration caused by the strong randomness of the next day's weather conditions, it is assumed that the forecast error follows a normal distribution, and an initial output scenario set is generated based on this distribution. Step S2. Construct a "reporting and pricing" mechanism for the total electricity volume and a monotonic reporting constraint, stipulating that thermal power units, new energy units, and load sides should all adopt the "reporting and pricing" model to participate in the total electricity volume market competition; Step S3. Provincial pre-clearance: Based on local unit bids and load forecasts, dispatching agencies in each province, with regional tie line power as a preset constraint boundary, conduct safety constraint unit combination and economic dispatch calculations based on the AC lossless network constraint model. Step S4. Centralized Clearing Between Provinces: The inter-provincial trading center receives the electricity surplus / deficit reported by each province and performs unified optimization and clearing. Step S5. Formal Clearance within the Province: Each province receives the tie-line adjustment plan, updates it to the system security-related constraints, and replaces the original tie-line pre-planned power.

2. The two-tier day-ahead spot market clearing method for renewable energy consumption as described in claim 1, characterized in that, Step S1 includes the following sub-steps: S1-1. Scene Dimensionality Reduction: The K-means clustering algorithm is used to reduce the massive initial scenes into multiple typical representative scenes, and the occurrence probability weight of each representative scene is calculated and obtained. S1-2. Probability Internalization: In the subsequent unified clearing calculation across multiple scenarios, each typical scenario and its probability weight are directly incorporated into the objective function of the market operator to drive the maximization of the expected value of social welfare across the entire network.

3. The two-tier day-ahead spot market clearing method for renewable energy consumption as described in claim 1, characterized in that, Step S2 includes the following sub-steps: S2-1. Data Structure: Thermal power units and load side adopt ordinary hourly segmented pricing, submitting aggregated data containing "price-electricity-time period" to clarify the correspondence between instantaneous electrical energy and settlement unit price within a specific time period; New energy units currently adopt a daily fixed pricing strategy. S2-2. Monotonicity constraint: The electricity price quotation curve of the generator set must follow the principle of monotony and non-decreasing; the electricity declaration curve on the user side must meet the principle of monotony and non-increasing.

4. The two-tier day-ahead spot market clearing method for renewable energy consumption according to claim 1, characterized in that, Step S3 includes the following sub-steps: S3-1. Establishment of the objective function: Establish a social welfare maximization objective function based on mixed integer linear programming, which comprehensively considers the probability weighted results under multiple scenarios; S3-2. Variable decoupling settings: The winning variables for power generation and load are accompanied by scenario index dimensions; the start-up and shutdown status of the units are set as variables without scenario factors; S3-3. Physical System Constraints: The model solution must satisfy the system power generation load balance constraint, the line power flow constraint based on the upper and lower limits of transmission capacity, and the upper and lower limits of section capacity application and output of thermal power units; when considering system safety-related constraints, the power of the regional tie line is used as the solution boundary within the province; S3-4. Electricity Price and Boundary Output: The initial nodal marginal electricity price, which accurately reflects the supply and demand relationship of the nodes, is calculated based on the power transfer distribution factor. Subsequently, the results of unit start-up and shutdown plans, the unabsorbed power generation surplus within the province, and the load deficit are extracted and passed to the inter-provincial day-ahead market model as boundary conditions.

5. A two-tier day-ahead spot market clearing method for renewable energy consumption according to claim 4, characterized in that, In step S3-1, the objective function for maximizing social welfare is: In the formula: Let S represent the output probability of each new energy scenario, and let S represent the set of all scenarios. express In this scenario, the electricity purchase cost for electricity buyer u at time t is given, where U represents the sum of costs paid by electricity retailers and wholesale users participating in the provincial day-ahead market, and T represents the sum of time periods. Let N represent the unit startup cost of thermal power unit i at time t in the given scenario, and let N represent the total number of thermal power units participating in the provincial day-ahead market. express The operating cost of thermal power unit i at time t in the given scenario; express The operating cost of new energy unit i at time t in the scenario, where NE represents the total number of new energy units; In step S3-3, the system power generation load balance constraint and line power flow constraint are as follows: In the formula: when conducting pre-clearance within the province, The planned power of regional link line j at time t; when the formal clearing within the province is carried out, Let be the adjusted power of the regional tie line j at time t; , Indicates the upper and lower limits of the transmission capacity of AC line l; express The power output of thermal power unit i at time t in the given scenario. express The power of the new energy unit i at time t in the given scenario. express The amount of electricity purchased by the electricity purchaser, u, at time t in the given scenario. , , These represent the set of thermal power units, the set of new energy power units, and the set of nodes, respectively.

6. The two-tier day-ahead spot market clearing method for renewable energy consumption according to claim 1, characterized in that, Step S4 includes the following sub-steps: S4-1 Objective function considering network losses and transmission costs: With the goal of maximizing social welfare, the objective function additionally considers the total transmission cost calculated based on the transfer distribution factor, and realizes the implicit auction of transmission rights by solving the optimization model; at the same time, line losses are considered in the system load balance constraints. S4-2 AC / DC hybrid power grid ATC constraints: Apply available transmission capacity constraints to prevent congestion: For DC tie lines: Constrain based on the node sensitivity distribution factor of the DC tie line, limiting it to the upper and lower limits of the available DC transmission capacity; For AC tie lines: Constrain based on the node sensitivity distribution factor of the AC tie line, limiting it to the upper and lower limits of the maximum forward / reverse available transmission capacity. S4-3 Settlement Mechanism and Instruction Issuance: A regional electricity price model is adopted, which treats a single province as an equivalent single node to obtain regional electricity price after clearing; after clearing is completed, the inter-provincial clearing and connection line adjustment plan is issued to each province.

7. A two-tier day-ahead spot market clearing method for renewable energy consumption as described in claim 6, characterized in that, In step S4-1, the objective function that takes into account network losses and transmission costs is: In the formula, Let S represent the output probability of each new energy scenario, and let S represent the set of all scenarios. express In this scenario, the electricity purchase cost for electricity buyer u at time t is given, where U represents the sum of electricity sales companies and wholesale users participating in the provincial day-ahead market, T represents the sum of time intervals, and N represents the total number of thermal power units participating in the provincial day-ahead market. express The operating cost of thermal power unit i at time t in the given scenario; express The operating cost of renewable energy unit i at time t in the given scenario, where NE represents the total number of renewable energy units. express The total transmission cost of the system in this scenario; the system load balancing constraint taking into account line losses is: In the formula: for In this scenario, the line loss (in MW) of the tie line h at time t; For the dual multiplier corresponding to the system power generation load balance constraint in time period t; In step S4-2, the power flow of the DC tie line satisfies: In the formula: Let represent the sensitivity distribution factor of node i to DC tie line d. , These represent the upper and lower limits of the available transmission capacity of the DC tie line d, respectively.

8. A two-tier day-ahead spot market clearing method for renewable energy consumption according to claim 1, characterized in that, Step S5 includes the following sub-steps: S5-1 Fixed unit start-up and shutdown status, that is, the 0-1 state variables calculated in step S3 are used as constant inputs; S5-2 restarts formal economic dispatch clearing calculations to determine the final winning unit output and calculate the final convergent nodal price across the entire network. This mechanism preserves the autonomy of each provincial dispatch center and forms a loosely coupled architecture of "boundary parameter transfer".

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