Wind-light-water-storage combined body spot market operation optimization scheduling strategy

Abstract

The invention discloses a wind-light-water-storage combined body spot market operation optimization scheduling strategy, and belongs to the technical field of electric power system operation and control. Aiming at the problems of fluctuation of wind-solar power generation and uncertainty of prediction errors, the invention proposes that a wind-solar group is combined with cascade hydropower and an energy storage pump station thereof, and a wind-solar-water storage combination body is constructed to participate in a multi-time-scale power market; by constructing an optimal scheduling model considering the source-price uncertainty, the comprehensive benefit maximization of the complex in the day-ahead market, the real-time market and the medium-and-long-term contract decomposition is realized; an information gap decision theory and a conditional value-at-risk method are adopted to ensure that the joint profit can still be ensured when the wind and light prediction error is expanded; meanwhile, profits are distributed fairly through a Shapley method, and the benefits of all subjects are improved; and the market competitiveness and executive force of wind and light power generation are effectively improved, the absorption and utilization of new energy are promoted, the flexibility and toughness of the system are high, and powerful support is provided for constructing a green, low-carbon and efficient modern energy system.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control technology, and in particular to an optimized dispatching strategy for spot market-oriented operation of a wind, solar, water and storage complex. Background Art

[0002] With the transformation of the global energy structure and the advancement of sustainable development goals, the construction of a new power system has become an irreversible trend. In this context, wind and solar power generation, as an important form of renewable energy, has been continuously expanding in breadth and depth in participating in power market transactions. In particular, wind and solar power generation participates in the medium- and long-term power market and connects with the spot market to form a multi-time scale market-based power balance mechanism, which has become an important way to promote the large-scale consumption of new energy.

[0003] However, the volatility of wind and solar power generation and the uncertainty of long-term forecast errors pose significant challenges to the decomposition and settlement of mid- and long-term market contracts. When facing high-proportion contract requirements, wind and solar power generation often face the risk of biased profit recovery during contract settlement due to forecast errors, which greatly reduces its profit margin. This situation seriously restricts the market competitiveness and sustainable development capabilities of wind and solar power generation.

[0004] In order to meet the above challenges and improve the market performance of wind and solar power generation, the industry generally adopts the strategy of combining wind and solar power generation with flexible regulating power sources to form a consortium. Taking hydropower as an example, as a typical flexible power source, cascade hydropower can effectively smooth out the volatility of wind and solar power generation through its flexible regulation capabilities, and improve the overall operational stability and market competitiveness of the consortium. At the same time, the introduction of energy storage pumping stations has further enhanced the regulation capabilities of cascade hydropower, making it possible to optimize the scheduling strategy of the wind, solar, water and storage consortium.

[0005] Although some studies have focused on the scheduling issues under the complementary multi-energy of hydropower and wind and solar power, for example, the 11th issue of Volume 53 of the Journal of Hydraulic Engineering explored the role of Longyangxia-Laxiwa cascade complementary energy storage in absorbing wind and solar power, but the research is mostly limited to the single dimension of the scheduling needs of the cascade hydropower itself and the minimization of the amount of wind and solar power abandoned, and failed to deeply explore the profitability of various entities from the perspective of electricity marketization. Similarly, although the 8th issue of Volume 54 of the Journal of Hydraulic Engineering further explored the short-term scheduling model of the combined operation of hybrid pumped storage and wind power after integration and transformation, its research focus is on the refined modeling of each unit, and does not fully consider the complex impact of electricity prices and wind and solar power uncertainties.

[0006] In summary, although the existing technology has explored the joint dispatching strategy of wind, solar and hydropower to a certain extent, it still has obvious deficiencies in considering the uncertainty of electricity price and wind and solar output, optimizing the operation of multi-time scale electricity market, and fairly distributing the profits within the consortium. Therefore, it is of great theoretical value and practical significance to construct an optimized dispatching strategy for the spot market operation of wind, solar and hydropower consortium that takes into account the uncertainty of wind and solar output and electricity price and fully considers the regulation capacity of cascade hydropower and energy storage pumping stations. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an optimized dispatching strategy for spot market-oriented operation of a wind, solar, water and storage combined power system, so as to solve the problem that when wind and solar power generation participate in the medium and long-term power market during the construction of a new power system, they are at a disadvantage when decomposing medium and long-term market contracts due to their volatility and uncertainty of long-term prediction errors, and face the risk of deviation profit recovery during contract settlement, thereby greatly compressing the profit space of new energy power generation.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is a wind, solar, water and storage joint spot market operation optimization scheduling strategy, including the following steps: Step 1: In the day-ahead market stage, the wind, solar, and hydropower consortium, as the recipient of the day-ahead market electricity price and the real-time market expected electricity price, conducts the decomposition and settlement of medium- and long-term contracts, and participates in day-ahead market transactions and intraday market expected transactions to maximize the overall benefits of the consortium's participation in the multi-time scale electricity market; Step 2: Construct a wind-solar-water-storage joint spot market transaction optimization decision model that takes into account the uncertainty of wind-solar output and electricity price. This model comprehensively considers the maximization of the comprehensive benefits of medium- and long-term contract settlement, day-ahead market operation and intraday market expected operation, as well as the profit maximization objective function of the wind-solar group's independent participation in the spot market. Its constraints include the decomposition and balance constraints of wind-solar medium- and long-term contracts, the day-ahead output constraints of wind-solar, and the day-ahead forecast error of wind-solar. Step 3: Establish a cascade hydropower independent participation in the day-ahead real-time market dispatch model based on conditional value at risk (CVaR), with the maximization of the comprehensive benefits of the cascade hydropower spot market as the objective function, and include the power generation output model of cascade hydropower stations and their operating boundary constraints, including the cascade reservoir water balance equation, cascade reservoir water level constraints, cascade reservoir discharge flow constraints, cascade hydropower station unit output constraints, cascade reservoir storage capacity constraints, cascade hydropower station output ramp constraints, and cascade hydropower medium- and long-term electricity decomposition constraints; Step 4: Using the Information Gap Decision Theory-Conditional Value at Risk (IGDT-CVaR) model, the impact of the uncertainty of wind-solar forecast errors on the dispatch decision of the consortium participating in the spot market is considered, and an optimal dispatch strategy model for the wind-solar-water storage consortium participating in the spot market is constructed, with the objective function of maximizing the degree of wind-solar fluctuations accepted by the consortium. Step 5: Determine the constraints of the optimal dispatching strategy model of the wind-solar-water-storage consortium, including the power composition of the consortium, the wind-solar prediction error and its variation range, the operation constraints of each power generation entity, and the constraints of the water balance of the cascade reservoirs considering the pumping station pumping; Step 6: Use the Shapley value method to fairly distribute the profits between the wind and solar power group and the cascade hydropower, so as to improve the interests of each entity compared with before the merger.

[0009] In the preferred solution, the calculation formula of the profit maximization objective function of the wind and solar group's independent participation in the spot market in step 2 is as follows: (1) Sampling clustering to generate real-time electricity price scenario set And its scenes Probability , with Fengguang Group independently participating in the spot market profit Maximum target, including day-ahead market profit , Profit decomposition of medium and long-term contracts , Excess profits from medium and long-term contracts return costs and real-time market profits ; In the formula, , and for Time period The day-ahead wind and solar power output plan, the decomposed output of medium- and long-term contracts, and the real-time output plan for the day under the scenario; for The mid- to long-term contracts are broken down into outputs; and The day-ahead electricity price and the real-time electricity price; The corresponding period Equal, representing and The medium and long term electricity price over time; is the transaction cycle; and is the time constant; is the transaction cycle; and That is, the positive price difference period and the negative price difference period; and That is, the cost of recovering the profits during the positive and negative price difference periods; and These are the upper and lower thresholds for benefit recovery; That is the day-ahead market forecast of electricity value.

[0010] In the preferred solution, the decomposition constraints of the wind and solar medium- and long-term contracts in Step 2 are expressed as follows: (2) In the formula, is the curve decomposition coefficient; is the load factor for each period; For any The remainder operation is performed during the time period.

[0011] In the preferred solution, the wind and solar power day-ahead output constraint condition and the wind and solar power day-ahead prediction error constraint condition in Step 2 are respectively expressed as: (3) (4) In the formula, and is the prediction error of wind power and photovoltaic power; and for The predicted wind power output and photovoltaic power output for the day before the time period; , They are Time period Wind power output and photovoltaic output in the scenario.

[0012] In the preferred solution, the calculation formula of the objective function of the cascade hydropower single participation in the day-ahead-real-time market dispatch model based on the conditional value at risk CVaR in Step 3 is: (5) In the formula, , and They are the day-ahead market profit, the profit from the decomposition of medium- and long-term contracts, and the real-time market profit when cascade hydropower bids separately; , and To decompose the output plan of cascade hydropower in the day-ahead, real-time and medium- and long-term contracts in response to the real-time electricity price scenario; is the risk value, is the risk preference coefficient, The larger the value, the more conservative and risk-averse the power generation entity is; the smaller the value, the more aggressive and profit-seeking the power generation entity is. is the VaR (Value at Risk) value, is an auxiliary variable, is the confidence level; through the second formula, we hope to optimize the scheduling plan for the scenario where the cost of cascade hydropower exceeds the VaR value; Profitable when bidding separately for cascaded hydropower; To consider the comprehensive benefits of cascade hydropower under risks, Represents any scene.

[0013] In the preferred solution, the power generation model of the cascade hydropower station considered in Step 3 is expressed as: (6) The parameters in the formula all correspond to Time period, To contribute to the hydropower station a few days ago, is the comprehensive output coefficient of the hydropower station, is the power generation flow, , For the day before The water level above the dam and tailwater level of each hydropower station, is the storage capacity on the previous day, is the discharge flow in the previous day, is the day-ahead power generation flow, is the previous day’s abandoned water flow, is the water level-reservoir capacity relationship function, It is the relationship function between tailwater level and downstream flow.

[0014] In the preferred solution, the water balance equation of the cascade reservoirs in Step 3 is: (7) In the formula, and Corresponding to and Time period Hydropower station storage capacity, For the day before The discharge flow of a hydropower station, is the runoff of the hydropower station.

[0015] In a preferred solution, the operating boundary constraints of the cascade hydropower stations in Step 3 include: (8) (9) (10) (11) (12) (13) In the formula, , For the The upper and lower limits of the water level of each hydropower station, , For the The upper and lower limits of the discharge capacity of each hydropower station, , For the The upper and lower limits of the output of each hydropower station, , For the The upper and lower limits of the storage capacity of a hydropower station, Representative The maximum climbing capacity of a hydropower station, for Time period The decomposition of medium and long-term contracts in the scenario; for The output of medium and long-term hydropower contracts is broken down into time periods. For the day before Output of hydropower station during the period.

[0016] In the preferred solution, the calculation formula of the objective function of the optimization scheduling strategy model for the wind, solar, water and storage consortium participating in the spot market in Step 4 is: (14) In the formula, is the range of wind and solar forecast error, To achieve profit, is the risk preference coefficient; , , and Real-time electricity price scenarios The consortium's day-ahead market profit, mid- to long-term contract decomposition profit, mid- to long-term contract excess profit return cost and real-time market profit; Risks faced when quoting for wind, solar and water joint ventures; , and They are the day-ahead output, real-time output, and output decomposition plan of the wind, solar and water consortium, respectively.

[0017] In the preferred solution, the constraint conditions of the optimization scheduling strategy model of the wind, solar, and water storage consortium in Step 5 are expressed as follows: (15) In the formula, , is the pumping power and pumping flow rate of the pumping station, is the maximum pumping flow rate of the pumping station, is the maximum output of the pump station, For the efficiency of the pump station, For the Hydropower Stations In the scenario Water level during the period, For the Hydropower Stations In the scenario Water level during the period, and For the Hydropower Stations In the scenario Power generation flow and abandoned water flow during the period, and is the actual prediction error of wind power and photovoltaic power, and is the range of wind power and photovoltaic prediction errors, and They are Wind power forecast error and photovoltaic forecast error during the period, Day 1 Hydropower Stations In the scenario Storage capacity during the period, Day 1 Hydropower Stations In the scenario Discharge flow during the period.

[0018] The present invention provides an optimized dispatching strategy for spot market operation of wind, solar, water and storage combined, which has the following beneficial effects: 1. The present invention solves the problem that when wind and solar power generation participate in the medium- and long-term power market in the construction of new power systems, due to its volatility and uncertainty of long-term prediction errors, it is at a disadvantage when decomposing medium- and long-term market contracts, and faces the risk of deviation profit recovery during contract settlement, thereby greatly compressing the profit space of new energy power generation, and also avoids the risk of abandoned power and water of cascade hydropower due to the uncontrollability and volatility of wind and solar power; 2. The construction of the multi-energy consortium of the present invention is different from the traditional single energy participating in the market. This strategy combines wind and solar power generation, cascade hydropower and energy storage equipment to form a consortium to jointly participate in the market, making full use of the complementarity of various energy sources, improving the overall market competitiveness and profitability through joint operation, and improving the flexibility and stability of the power system; 3. The present invention is based on the information gap decision theory and the optimization model of conditional risk value. The strategy introduces the IGDT-CVaR model, which is a new method for dealing with uncertainty and risk. It can formulate the optimal dispatching strategy under the condition of considering the uncertainty of wind and solar power output and electricity price. This method is rarely used in the optimization dispatching of the power market, so it has a high novelty. 4. The application of the energy storage pump station of the present invention is to install an energy storage pump station between the upstream and downstream reservoirs of the cascade hydropower to improve the flexibility of hydropower resources. This innovative application method further explores the regulation potential of cascade hydropower and provides a new solution for the flexible dispatch of the power system; 5. The fair profit distribution mechanism of the present invention uses the Shapley value method to fairly distribute the profits between the wind and solar power group and the cascade hydropower, ensuring that the interests of each entity are improved compared with before the combination. This mechanism not only ensures the stability of the combination, but also promotes the development of new energy; 6. The present invention improves the overall competitiveness and execution of wind and solar power generation through the construction of a wind, solar, and water storage consortium; 7. The present invention comprehensively considers the uncertainty of wind and solar power output and electricity prices, and realizes the optimal scheduling and operation of the multi-time scale power market; 8. The present invention ensures the improvement of the interests of wind and solar power groups and cascade hydropower in joint operations through fair profit distribution and risk avoidance strategies; 9. The present invention maximizes the benefits of the wind, solar and water consortium through joint bidding and optimized scheduling strategies, which helps to promote the further development and application of new energy; 10. The present invention is also highly scalable and flexible, and can adapt to different geographical environments and energy distribution characteristics, providing strong support for building a green, low-carbon, efficient and reliable energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 The wind, solar, and water storage consortium of the present invention participates in the spot market trading framework; Figure 2 The mid- and long-term curve decomposition results of the consortium under different curve decomposition coefficients of the present invention; Figure 3 This is the day-ahead real-time optimization scheduling result of the wind, solar, and water storage system of the present invention; Figure 4 The comprehensive benefits corresponding to different risk preference coefficients and profit requirement ratios of the present invention; Figure 5 It is the variation range of wind / solar forecast error corresponding to different risk preference coefficients and profit requirement multiples of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is further described below in conjunction with the embodiments and drawings: Example 1 like Figure 1 As shown, a spot market-oriented operation optimization scheduling strategy for wind, solar, water and storage combined systems includes the following steps: Step 1: In the day-ahead market stage, the wind, solar, and hydropower consortium, as the recipient of the day-ahead market electricity price and the real-time market expected electricity price, conducts the decomposition and settlement of medium- and long-term contracts, and participates in day-ahead market transactions and intraday market expected transactions to maximize the overall benefits of the consortium's participation in the multi-time scale electricity market; Step 2: Construct a wind-solar-water-storage joint spot market transaction optimization decision model that takes into account the uncertainty of wind-solar output and electricity price. This model comprehensively considers the maximization of the comprehensive benefits of medium- and long-term contract settlement, day-ahead market operation and intraday market expected operation, as well as the profit maximization objective function of the wind-solar group's independent participation in the spot market. Its constraints include the decomposition and balance constraints of wind-solar medium- and long-term contracts, the day-ahead output constraints of wind-solar, and the day-ahead forecast error of wind-solar. Step 3: Establish a dispatch model for cascade hydropower to participate in the day-ahead-real-time market based on the conditional value at risk (CVaR), with the maximization of the comprehensive benefits of the cascade hydropower spot market as the objective function, and include the power generation output model of cascade hydropower stations and their operating boundary constraints, including the cascade reservoir water balance equation, cascade reservoir water level constraints, cascade reservoir discharge flow constraints, cascade hydropower station unit output constraints, cascade reservoir storage capacity constraints, cascade hydropower station output ramp constraints, and cascade hydropower medium- and long-term electricity decomposition constraints; Step 4: Using the information gap decision theory-conditional value at risk IGDT-CVaR model, considering the impact of wind-solar forecast error uncertainty on the consortium's decision-making in the spot market, an optimal dispatching strategy model for the wind-solar-water consortium to participate in the spot market is constructed, with the objective function of maximizing the degree of wind-solar fluctuations accepted by the consortium; Step 5: Determine the constraints of the optimal dispatching strategy model of the wind-solar-water-storage consortium, including the power composition of the consortium, the wind-solar prediction error and its variation range, the operation constraints of each power generation entity, and the constraints of the water balance of the cascade reservoirs considering the pumping station pumping; Step 6: Use the Shapley value method to fairly distribute the profits between the wind and solar power group and the cascade hydropower, so as to improve the interests of each entity compared with before the merger.

[0021] In this embodiment, the calculation formula of the profit maximization objective function of the wind and solar group's independent participation in the spot market in step 2 is as follows: (1) Sampling clustering to generate real-time electricity price scenario set And its scenes Probability , with Fengguang Group independently participating in the spot market profit Maximum target, including day-ahead market profit , Profit decomposition of medium and long-term contracts , Excess profits from medium and long-term contracts return costs and real-time market profits ; In the formula, , and for Time period The day-ahead wind and solar power output plan, the decomposed output of medium- and long-term contracts, and the real-time output plan for the day under the scenario; for The mid- to long-term contracts are broken down into outputs; and The day-ahead electricity price and the real-time electricity price; The corresponding period Equal, representing and The medium and long term electricity price over time; is the transaction cycle; and is the time constant, set to 1 hour and 15 minutes; For the trading cycle, it is set to 96 periods of the target day; and That is, the positive price difference period and the negative price difference period; and That is, the cost of recovering the profits during the positive and negative price difference periods; and These are the upper and lower thresholds for benefit recovery; That is the day-ahead market forecast of electricity value.

[0022] Furthermore, the decomposition constraints of the wind and solar medium- and long-term contracts in Step 2 are expressed as follows: (2) In the formula, is the curve decomposition coefficient; is the load factor for each period; For any The remainder operation is performed during the time period.

[0023] Furthermore, the wind and solar power day-ahead output constraint and the wind and solar power day-ahead prediction error constraint in Step 2 are respectively expressed as: (3) (4) In the formula, and is the prediction error of wind power and photovoltaic power; and for The predicted wind power output and photovoltaic power output for the day before the time period; , They are Time period Wind power output and photovoltaic output in the scenario.

[0024] Furthermore, the calculation formula of the objective function of the cascade hydropower single participation in the day-ahead-real-time market dispatch model based on the conditional value at risk CVaR in Step 3 is: (5) In the formula, , and They are the day-ahead market profit, the profit from the decomposition of medium- and long-term contracts, and the real-time market profit when cascade hydropower bids separately; , and To decompose the output plan of cascade hydropower in the day-ahead, real-time and medium- and long-term contracts in response to the real-time electricity price scenario; is the risk value, is the risk preference coefficient, The larger the value, the more conservative and risk-averse the power generation entity is; the smaller the value, the more aggressive and profit-seeking the power generation entity is. is the VaR value, is an auxiliary variable, is the confidence level; through the second formula, we hope to optimize the scheduling plan for the scenario where the cost of cascade hydropower exceeds the VaR value; Profitable when bidding separately for cascaded hydropower; To consider the comprehensive benefits of cascade hydropower under risks, Represents any scene.

[0025] Furthermore, the power generation model of the cascade hydropower station considered in Step 3 is expressed as: (6) The parameters in the formula all correspond to Time period, To contribute to the hydropower station a few days ago, is the comprehensive output coefficient of the hydropower station, is the power generation flow, , For the day before The water level above the dam and tailwater level of each hydropower station, is the storage capacity on the previous day, is the discharge flow in the previous day, is the day-ahead power generation flow, is the previous day’s abandoned water flow, is the water level-reservoir capacity relationship function, It is the relationship function between tailwater level and downstream flow.

[0026] Furthermore, the water balance equation of the cascade reservoirs in Step 3 is: (7) In the formula, and Corresponding to and Time period Hydropower station storage capacity, For the day before The discharge flow of a hydropower station, is the runoff of the hydropower station.

[0027] Furthermore, the operating boundary constraints of the cascade hydropower stations in Step 3 include: (8) (9) (10) (11) (12) (13) In the formula, , For the The upper and lower limits of the water level of each hydropower station, , For the The upper and lower limits of the discharge capacity of each hydropower station, , For the The upper and lower limits of the output of each hydropower station, , For the The upper and lower limits of the storage capacity of a hydropower station, Representative The maximum climbing capacity of a hydropower station, for Time period The decomposition of medium and long-term contracts in the scenario; for The output of medium and long-term hydropower contracts is broken down into time periods. For the day before Output of hydropower station during the period.

[0028] Furthermore, the calculation formula of the objective function of the optimization scheduling strategy model for the wind-solar-water-storage consortium participating in the spot market in Step 4 is: (14) In the formula, is the range of wind and solar forecast error, To achieve profit, is the risk preference coefficient; , , and Real-time electricity price scenarios The consortium's day-ahead market profit, mid- to long-term contract decomposition profit, mid- to long-term contract excess profit return cost and real-time market profit; Risks faced when quoting for wind, solar and water joint ventures; , and They are the day-ahead output, real-time output, and output decomposition plan of the wind, solar and water consortium, respectively.

[0029] Furthermore, the constraints of the optimization scheduling strategy model of the wind, solar, and water storage consortium in Step 5 are expressed as follows: (15) In the formula, , is the pumping power and pumping flow rate of the pumping station, is the maximum pumping flow rate of the pumping station, is the maximum output of the pump station, For the efficiency of the pump station, For the Hydropower Stations In the scenario Water level during the period, For the Hydropower Stations In the scenario Water level during the period, and For the Hydropower Stations In the scenario Power generation flow and abandoned water flow during the period, and is the actual prediction error of wind power and photovoltaic power, and is the range of wind power and photovoltaic prediction errors, and They are Wind power forecast error and photovoltaic forecast error during the period, Day 1 Hydropower Stations In the scenario Storage capacity during the period, Day 1 Hydropower Stations In the scenario Discharge flow during the period.

[0030] Example 2 In another preferred embodiment, based on the above embodiment 1, Figure 1 As shown, a specific embodiment is provided below, a spot market-oriented operation optimization scheduling strategy of a wind, solar, water and storage joint venture, including: 1. Wind and Solar Group’s pre-independence day and intraday scheduling model Sampling clustering to generate real-time electricity price scenario set And its scenes Probability , construct an independent day-ahead and day-intraday dispatch model for wind and solar groups considering the uncertainty of wind and solar power and the uncertainty of intraday electricity prices.

[0031] 1) Objective function Sampling clustering to generate real-time electricity price scenario set And its scenes Probability , with Fengguang Group independently participating in the spot market profit Maximum target, including day-ahead market profit , Profit decomposition of medium and long-term contracts , Excess profits from medium and long-term contracts return costs and real-time market profits ; In the formula, , and for Time period The day-ahead wind and solar power output plan, the decomposed output of medium- and long-term contracts, and the real-time output plan for the day under the scenario; for The mid- to long-term contracts are broken down into outputs; and The day-ahead electricity price and the real-time electricity price; The corresponding period equal; is the transaction cycle; and Represents 1 hour and 15 minutes. is the trading cycle (96 periods on the underlying day); and That is, the positive price difference period and the negative price difference period; and That is, the cost of recovering the profits during the positive and negative price difference periods; and These are the upper and lower thresholds for benefit recovery; That is the day-ahead market forecast of electricity value.

[0032] (16) 2) Constraints (1) Decomposition balance and decomposition constraints of wind and solar medium- and long-term contracts The medium- and long-term wind and solar time-of-use contracts are equal to the sum of the 15-minute electricity plans decomposed into the corresponding hours. In order to prevent the power generation side from concentrating all the electricity in one hour in a certain period for profit maximization, thereby damaging the interests of the power consumption side, this stage requires that the proportion of electricity decomposed in each period shall not be less than a certain multiple of the total hourly electricity multiplied by the load factor as a contract decomposition constraint, expressed as: (2) In the formula, is the curve decomposition coefficient; is the load factor for each period.

[0033] (2) Wind and solar power output constraints on the day before The wind and solar power output plan does not exceed its day-ahead forecast value: (3) (3) Wind and solar day-ahead forecast error The wind and solar forecast error is the difference between the real-time wind and solar output on day D and its day-ahead output plan: (4) In the formula, and is the prediction error of wind power and photovoltaic power.

[0034] 2. CVaR-based cascade hydropower single participation in the day-ahead-real-time market dispatch model The conditional value at risk (CVaR) is used to measure the impact of spot market electricity price uncertainty on the profitability of cascade hydropower spot market.

[0035] 1) Objective function Comprehensive benefits of cascade hydropower spot market Maximization is the objective function, including spot market profit and risk cost, which can be expressed as: (17) In the formula, , and They are the day-ahead market profit, the profit from the decomposition of medium- and long-term contracts, and the real-time market profit when cascade hydropower bids separately; , and To decompose the output plan of cascade hydropower in the day-ahead, real-time and medium- and long-term contracts in response to the real-time electricity price scenario; is the risk value, is the risk measurement coefficient, The larger the value, the more conservative and risk-averse the power generation entity is; the smaller the value, the more aggressive and profit-seeking the power generation entity is.

[0036] Based on CVaR The calculation formula is expressed as: (18) In the formula, is the VaR value, is an auxiliary variable, is the confidence level; through the second formula, we hope to optimize the scheduling plan for the scenario where the cost of cascade hydropower exceeds the VaR value.

[0037] 2) Power generation output model of cascade hydropower stations (6) In the formula, To contribute to the hydroelectric power station, is the comprehensive output coefficient of the hydropower station, is the power generation flow, , For the The water level above the dam and tailwater level of each hydropower station, is the storage capacity, is the discharge flow, The discarded water flow.

[0038] 3) Constraints (1) Water balance equation of cascade reservoirs (7) In the formula, and Corresponding to and Time period The storage capacity of a hydropower station.

[0039] (2) Operation boundary constraints of cascade hydropower stations Cascade reservoir water level constraints: (8) In the formula, , For the The upper and lower limits of water levels at hydropower stations.

[0040] Constraints on discharge flow from cascade reservoirs: (9) In the formula , For the The upper and lower limits of the discharge capacity of a hydropower station.

[0041] Output constraints of cascade hydropower station units: (10) In the formula, , For the The upper and lower limits of the output of a hydropower station.

[0042] Cascade reservoir capacity constraints: (11) In the formula, , For the The upper and lower limits of the storage capacity of a hydropower station.

[0043] (3) Output ramp constraints of cascade hydropower stations (12) In the formula, Representative The maximum climbing capacity of a hydropower station.

[0044] (4) Constraints on medium- and long-term electricity consumption decomposition of cascade hydropower (13) In the formula, for Output breakdown of medium- and long-term hydropower contracts during time periods.

[0045] 3. Optimal dispatch strategy for wind, solar, water and storage consortiums participating in the spot market, taking into account profit pursuit and risk aversion The Information Gap Decision Theory (IGDT) is used to consider the impact of the uncertainty of wind-solar forecast errors on the consortium's decision-making in the spot market. The characteristic of IGDT is that it is suitable for situations with large uncertainty and difficult to describe with probability distribution. It considers the decision-making method of the "worst case" that may occur within a given uncertainty range, and can provide two different decision-making strategies: profit pursuit and risk avoidance. Considering that this paper is not only about risk avoidance, but also about exploring the absorption limit of cascade energy storage regulation on wind and solar, this paper combines the two, and uses the IGDT risk aversion model as the framework to explore the maximum fluctuation range of wind-solar forecast errors that the consortium can accept when the comprehensive benefits of the cascade energy storage and wind-solar groups are expanded by a given multiple compared to independent decision-making. That is, when the wind-solar forecast error is expanded to the original Double times, ensure that the joint comprehensive benefits of both parties under the decision solution are not less than .

[0046] 1) The objective function is expressed as follows: (19) In the formula, is the range of wind and solar forecast error, To achieve profit, is the risk preference coefficient; The overall benefits of the consortium taking into account risk costs.

[0047] (20) In the formula, , , and Real-time electricity price scenarios The consortium's day-ahead market profit, mid- to long-term contract decomposition profit, mid- to long-term contract excess profit return cost and real-time market profit; Risks faced when quoting for wind, solar and water joint ventures; , and They are the day-ahead output, real-time output, and mid- to long-term contract output plans of the wind, solar, and hydropower consortium; and is the actual prediction error of wind power and photovoltaic power, and is the range of wind power and photovoltaic prediction errors.

[0048] 2) Constraints (1) Power composition of the consortium (twenty one) (2) Wind and solar forecast error and its range of variation (twenty two) (3) Operation constraints of each power generation entity The operating constraints of wind and solar power units are the same as those of equations (2) to (4), and the operating constraints of cascade hydropower are the same as those of equations (7) to (13). The water balance equation needs to be corrected to consider the influence of pumping station water pumping, as shown in the following equation (23).

[0049] (4) Water balance of cascade reservoirs considering pumping stations In actual operation, the existing power market often experiences long periods of negative electricity prices due to strong wind and solar power generation. In order to absorb wind and solar power and provide more flexibility for the power system when there is a negative electricity price, the energy storage pump station will use the excess abandoned energy of wind and solar power as a flexible resource to adjust the output of wind and solar power. The energy consumption model of the pump station is expressed as: (twenty three) In the formula, For the efficiency of the pump station, For the Hydropower Stations In the scenario Water level during the period; , The pumping power and pumping flow of the pumping station; This affects the water balance equation of the upstream and downstream reservoirs, as shown in equations (35)(45).

[0050] (twenty four) In the formula, and For the Hydropower Stations In the scenario The power generation flow and abandoned water flow in the period.

[0051] The pumping flow constraint of the pumping station is: (25) In the formula, It is the maximum pumping flow rate of the pumping station.

[0052] The output constraint of the pump station is: (26) In the formula, It is the maximum output of the pump station.

[0053] Example 3 In another preferred embodiment, based on the above embodiments 1 and 2, the implementation of a wind-solar-water-storage joint spot market operation optimization scheduling strategy is discussed in detail, which includes: The information gap decision theory IGDT is used to consider the impact of the uncertainty of wind-solar forecast error on the consortium's decision-making in the spot market. The characteristic of IGDT is that it is suitable for situations with large uncertainty and difficult to describe with probability distribution. It considers the decision-making method of the "worst case" that may occur within a given uncertainty range, and can provide two different decision-making strategies: profit pursuit and risk avoidance. Considering that this paper is not only about risk avoidance, but also about exploring the absorption limit of cascade energy storage regulation on wind and solar, this paper combines the two, and uses the IGDT risk aversion model as the framework to explore the maximum fluctuation range of wind-solar forecast error that the consortium can accept when the comprehensive benefits of the cascade energy storage and wind-solar group are expanded by a given multiple compared with independent decision-making. That is, when the wind-solar forecast error is expanded to the original Double times, ensure that the joint comprehensive benefits of both parties under the decision solution are not less than : (19) In the formula, is the range of wind and solar forecast error, To achieve profit, is the risk preference coefficient; The overall benefits of the consortium taking into account risk costs.

[0054] (20) In the formula, , , and Real-time electricity price scenarios The consortium's day-ahead market profit, mid- to long-term contract decomposition profit, mid- to long-term contract excess profit return cost and real-time market profit; Risks faced when quoting for wind, solar and water joint ventures; , and They are the day-ahead output, real-time output, and mid- to long-term contract output plans of the wind, solar, and hydropower consortium; and is the actual prediction error of wind power and photovoltaic power, and is the range of wind power and photovoltaic prediction errors.

[0055] (1) Power composition of the consortium (twenty one) (2) Wind and solar forecast error and its range of variation (twenty two) (3) Operation constraints of each power generation entity The operating constraints of wind and solar power units are the same as those of equations (2) to (4), and the operating constraints of cascade hydropower are the same as those of equations (7) to (13). The water balance equation needs to be corrected to consider the influence of pumping station water pumping, as shown in the following equation (23).

[0056] (4) Water balance of cascade reservoirs considering pumping stations In actual operation, the existing power market often experiences long periods of negative electricity prices due to the large-scale generation of wind and solar power. In order to absorb wind and solar power and provide more flexibility for the power system when the electricity price is negative, the energy storage pump station will use the excess abandoned energy of wind and solar power as a flexible resource to adjust the output of wind and solar power. The energy consumption model of the pump station is expressed as: (twenty three) In the formula, , The pumping power and pumping flow of the pumping station; This affects the water balance equation of the upstream and downstream reservoirs. is the maximum output of the pump station, For the efficiency of the pump station, For the Hydropower Stations In the scenario Water level during the period, and For the Hydropower Stations In the scenario The power generation flow and abandoned water flow in each period.

[0057] (twenty four) The pumping flow constraint of the pumping station is: (25) In the formula, It is the maximum pumping flow rate of the pumping station.

[0058] The output constraint of the pump station is: (26) The results show that if Figure 2 As shown in the figure, when the curve decomposition coefficient increases, the consortium tends to decompose the medium- and long-term electricity in the period of positive price difference; the example shows that this strategy will significantly improve the profit of the consortium; but the more the power generation side entities concentrate on decomposing electricity under positive price difference, the greater the loss for the power consumption side; and the medium- and long-term transactions and D-2 day decisions are traded on an hourly basis, which is different from the 15-minute trading scale on D-1; although the medium- and long-term time-segment transactions lock in the difference contract electricity in each period, the more frequent trading scale will inevitably bring higher risks to both the power generation and the power consumption sides; in order to avoid the power consumption side entities from suffering large losses due to the concentrated decomposition of electricity by the power generation side at the high point of the price difference within each hour, the curve decomposition coefficient should not be taken at a higher value in practice.

[0059] Select as is 4, is 0, The profit pursuit model solution under -0.3 is analyzed, that is, the optimal dispatch of the wind, solar, and water storage consortium without considering the risk; at this time, the solution is is 0.3554, that is, when the wind-solar forecast error is 35.54% larger than the original error, the comprehensive benefit can still be increased by 30% compared with the two parties being separated; Figure 3 As shown in the figure, there are significant differences between the day-ahead dispatch and real-time dispatch of the wind, solar, and hydropower consortium. When the day-ahead electricity price is positive and the real-time electricity price is negative, the power system often faces a problem of excess electricity due to low load and large-scale development of new energy. At this time, the power generation side needs to reduce output. According to the dispatch results, the consortium responds to positive and negative electricity prices from two aspects. On the one hand, the consortium will choose to shrink the real-time market output at this time. Figure 3It can be seen that the real-time market output during the period of negative electricity price generally decreases compared with the previous day; on the other hand, the consortium will choose to activate the pump station at this time to obtain market profits under the negative electricity price; in addition, the consortium is limited by the ramp rate limit of the hydropower unit. When the real-time market has frequent positive and negative changes, it will selectively follow the changes in electricity prices to reasonably arrange the output; for example, in periods 2 to 5, the cascade hydropower is in full power state in period 2, and in period 3, the cascade hydropower responds to the negative electricity price and reduces its output to 100MW and 200MW. In period 4, when the electricity price is positive, it does not restore its output to full power state, but only increases it to 200MW and 300MW; the reason is that the real-time market in period 5 has negative electricity price again; if it is increased to full power state, it cannot fully reduce the output in this period, resulting in profit loss.

[0060] Different risk preferences affect the consortium's decision-making in the medium- and long-term market, the day-ahead market, and the real-time market, and the comprehensive benefits of its decision-making results are limited by the required profit multiple; therefore, the risk preference coefficient and profit requirement ratio Directly determine the maximum range of wind and solar fluctuations that the consortium can accept under given profit requirements; and show different and Downwind solar forecast error variation range and comprehensive benefits; Figure 4 It can be seen that under a certain risk preference coefficient, the higher the profit requirement, the greater the corresponding comprehensive benefits of the wind, solar and water storage system. And as the risk preference coefficient continues to rise, the consortium's scheduling and trading decisions in the two spot markets become more conservative, and thus its comprehensive benefits continue to decline; therefore, from the perspective of interests, the risk preference coefficient and profit requirement ratio Both have an impact on the consortium when making decisions; Figure 5 A similar conclusion can be drawn. Since there is an upper limit to the regulating effect of cascaded energy storage on wind and solar power, the higher the profit requirements, the smaller the range of wind and solar power prediction errors that wind, solar and hydropower storage can accept.

[0061] In summary, this paper proposes an optimized dispatching strategy for spot market operation of wind, solar, water and storage combined power plants. The following conclusions are drawn through case analysis: (1) In the optimized dispatching strategy of the wind, solar and water consortium before D-1, the curve decomposition coefficient has a significant impact on the profitability of the consortium's CFD. When wind and solar participate in the market alone, due to the existence of forecast errors and the inability to adjust themselves, the profitability of wind and solar participating in the electricity market is greatly reduced; (2) By regulating the output of renewable energy through cascade hydropower and energy storage pump stations, the benefits of wind and solar power participating in the real-time market can be effectively improved, and the profitability of the consortium can still be guaranteed when the range of wind and solar power forecast errors increases; (3) Adding energy storage pumping stations between the upstream and downstream reservoirs of cascade hydropower can make more full use of the hydropower resources of cascade hydropower. Calculation examples show that under ideal conditions, the profit can be increased by 14% compared with not installing pumping stations.

[0062] The present invention comprehensively considers the uncertainty of medium- and long-term contracts, day-ahead markets, real-time markets, and wind and solar output and electricity prices, constructs a multi-time scale optimization scheduling model, and maximizes the interests of multiple subjects. This multi-dimensional optimization scheduling method is relatively rare in the power market and shows a high degree of creativity; through the IGDT-CVaR model, this strategy can find a balance between profit pursuit and risk aversion, ensuring that the consortium can still maintain a high profitability in an uncertain market environment. The proposal of this balance strategy is an important supplement and innovation to the traditional power market scheduling method; it introduces energy storage pumping stations into the cascade hydropower system and integrates them with wind and solar power generation and cascade hydropower. The integrated optimization improves the flexibility and regulation ability of the entire system. This integration method not only solves the volatility problem of wind and solar power generation, but also provides new technical means for the flexible scheduling of power systems, showing a high degree of creativity. It uses the Shapley method to fairly distribute profits, which not only ensures the stability of the consortium, but also promotes the development of new energy. This innovation in the profit distribution mechanism is an important supplement and improvement to the existing power market operation mechanism. In addition, the model also innovatively introduces machine learning algorithms for predictive optimization, accurately predicts wind and solar output and electricity price fluctuations, further improves the accuracy and foresight of scheduling decisions, and sets a benchmark for the intelligent transformation of the power market.

Claims

1. A wind, solar, water and storage joint spot market operation optimization scheduling strategy, characterized by: The strategy includes the following steps: Step 1: In the day-ahead market stage, the wind, solar, and hydropower consortium, as the recipient of the day-ahead market electricity price and the real-time market expected electricity price, conducts the decomposition and settlement of medium- and long-term contracts, and participates in day-ahead market transactions and intraday market expected transactions to maximize the overall benefits of the consortium's participation in the multi-time scale electricity market; Step 2: Construct a wind-solar-water-storage joint spot market transaction optimization decision model that takes into account the uncertainty of wind-solar output and electricity price. This model comprehensively considers the maximization of the comprehensive benefits of medium- and long-term contract settlement, day-ahead market operation and intraday market expected operation, as well as the profit maximization objective function of the wind-solar group's independent participation in the spot market. Its constraints include the decomposition and balance constraints of wind-solar medium- and long-term contracts, the day-ahead output constraints of wind-solar, and the day-ahead forecast error of wind-solar. Step 3: Establish a dispatch model for cascade hydropower to participate in the day-ahead-real-time market based on the conditional value at risk (CVaR), with the maximization of the comprehensive benefits of the cascade hydropower spot market as the objective function, and include the power generation output model of cascade hydropower stations and their operating boundary constraints, including the cascade reservoir water balance equation, cascade reservoir water level constraints, cascade reservoir discharge flow constraints, cascade hydropower station unit output constraints, cascade reservoir storage capacity constraints, cascade hydropower station output ramp constraints, and cascade hydropower medium- and long-term electricity decomposition constraints; Step 4: Using the information gap decision theory-conditional value at risk IGDT-CVaR model, considering the impact of wind-solar forecast error uncertainty on the consortium's decision-making in the spot market, an optimal dispatching strategy model for the wind-solar-water consortium to participate in the spot market is constructed, with the objective function of maximizing the degree of wind-solar fluctuations accepted by the consortium; Step 5: Determine the constraints of the optimal dispatching strategy model of the wind-solar-water-storage consortium, including the power composition of the consortium, the wind-solar prediction error and its variation range, the operation constraints of each power generation entity, and the constraints of the water balance of the cascade reservoirs considering the pumping station pumping; Step 6: Use the Shapley value method to fairly distribute the profits between the wind and solar power group and the cascade hydropower, so as to improve the interests of each entity compared with before the merger.

2. According to claim 1, a spot market-oriented operation optimization scheduling strategy for wind, solar, water and storage combined enterprises is characterized by: The calculation formula of the profit maximization objective function of Fengguang Group's independent participation in the spot market in step 2 is as follows: （1）； Sampling clustering to generate real-time electricity price scenario set And its scenes Probability , with Fengguang Group independently participating in the spot market profit Maximum target, including day-ahead market profit , Profit decomposition of medium and long-term contracts , Excess profits from medium and long-term contracts return costs and real-time market profits ; In the formula, , and for Time period The day-ahead wind and solar power output plan, the decomposed output of medium- and long-term contracts, and the real-time output plan for the day under the scenario; for The mid- to long-term contracts are broken down into outputs; and The day-ahead electricity price and the real-time electricity price; The corresponding period Equal, representing and The medium and long term electricity price over time; is the transaction cycle; and is the time constant; is the transaction cycle; and That is, the positive spread period and the negative spread period; and That is, the cost of recovering the profits of the wind and solar power during the positive and negative price difference periods; and These are the upper and lower thresholds for benefit recovery; That is the day-ahead market forecast of electricity value.

3. According to claim 1, a spot market-oriented operation optimization scheduling strategy for wind, solar, water and storage combined enterprises is characterized by: The decomposition constraints of the wind and solar medium- and long-term contracts in Step 2 are expressed as follows: （2）； In the formula, is the curve decomposition coefficient; is the load factor for each period; For any The remainder operation is performed during the time period.

4. According to claim 1, a spot market-oriented operation optimization scheduling strategy for wind, solar, water and storage combined enterprises is characterized by: The wind and solar power day-ahead output constraint and the wind and solar power day-ahead prediction error constraint in Step 2 are respectively expressed as: （3）； （4）； In the formula, and is the prediction error of wind power and photovoltaic power; and for The predicted wind power output and photovoltaic power output for the day before the time period; , They are Time period Wind power output and photovoltaic output in the scenario.

5. According to claim 1, a spot market-oriented operation optimization scheduling strategy for wind, solar, water and storage combined enterprises is characterized by: The calculation formula of the objective function of the cascade hydropower single participation in the day-ahead-real-time market dispatch model based on the conditional value at risk CVaR in Step 3 is: （5）； In the formula, , and They are the day-ahead market profit, the profit from the decomposition of medium- and long-term contracts, and the real-time market profit when cascade hydropower bids separately; , and To decompose the output plan of cascade hydropower in the day-ahead, real-time and medium- and long-term contracts in response to the real-time electricity price scenario; is the risk value, is the risk preference coefficient, The larger the value, the more conservative and risk-averse the power generation entity is; the smaller the value, the more aggressive and profit-seeking the power generation entity is. is the VaR value, is an auxiliary variable, is the confidence level; through the second formula, we hope to optimize the scheduling plan for the scenario where the cost of cascade hydropower exceeds the VaR value; Profitable when bidding separately for cascaded hydropower; To consider the comprehensive benefits of cascade hydropower under risks, Represents any scene.

6. The optimized scheduling strategy for spot market operation of wind, solar, water and storage complex according to claim 1 is characterized by: The power generation model of cascade hydropower stations considered in Step 3 is expressed as: （6）； The parameters in the formula all correspond to Time period, To contribute to the hydropower station a few days ago, is the comprehensive output coefficient of the hydropower station, is the power generation flow, , For the day before The water level above the dam and tailwater level of each hydropower station, is the storage capacity on the previous day, is the discharge flow in the previous day, is the day-ahead power generation flow, is the previous day’s abandoned water flow, is the water level-reservoir capacity relationship function, It is the relationship function between tailwater level and downstream flow.

7. The optimized scheduling strategy for spot market operation of wind, solar, water and storage complex according to claim 1 is characterized by: The water balance equation of the cascade reservoirs in Step 3 is: （7）； In the formula, and Corresponding to and Time period Hydropower station storage capacity, For the day before The discharge flow of a hydropower station, is the runoff of the hydropower station.

8. The optimized scheduling strategy for spot market operation of wind, solar, water and storage complex according to claim 1 is characterized by: The operation boundary constraints of the cascade hydropower stations in Step 3 include: （8）； （9）； （10）； （11）； （12）； （13）； In the formula, , For the The upper and lower limits of the water level of each hydropower station, , For the The upper and lower limits of the discharge capacity of each hydropower station, , For the The upper and lower limits of the output of each hydropower station, , For the The upper and lower limits of the storage capacity of a hydropower station, Representative The maximum climbing capacity of a hydropower station, for Time period The decomposition of medium and long-term contracts in the scenario; for The output of medium and long-term hydropower contracts is broken down into time periods. For the day before Output of hydropower station during the period.

9. The optimized scheduling strategy for spot market operation of wind, solar, water and storage complex according to claim 1 is characterized by: The calculation formula of the objective function of the optimization scheduling strategy model of the wind-solar-water-storage consortium participating in the spot market in Step 4 is: （14）； In the formula, is the range of wind and solar forecast error, To achieve profit, is the risk preference coefficient; , , and Real-time electricity price scenarios The consortium's day-ahead market profit, mid- to long-term contract decomposition profit, mid- to long-term contract excess profit return cost and real-time market profit; Risks faced when quoting for wind, solar and water joint ventures; , and They are the day-ahead output, real-time output, and output decomposition plan of the wind, solar and water consortium, respectively.

10. The optimized scheduling strategy for spot market operation of wind, solar, water and storage combined power plant according to claim 1 is characterized by: The constraints of the optimization scheduling strategy model of the wind-solar-water-storage consortium in Step 5 are expressed as follows: （15）； In the formula, , is the pumping power and pumping flow rate of the pumping station, is the maximum pumping flow rate of the pumping station, is the maximum output of the pump station, For the efficiency of the pump station, For the Hydropower Stations In the scenario Water level during the period, For the Hydropower Stations In the scenario Water level during the period, and For the Hydropower Stations In the scenario Power generation flow and abandoned water flow during the period, and is the actual prediction error of wind power and photovoltaic power, and is the range of wind power and photovoltaic prediction errors, and They are Wind power forecast error and photovoltaic forecast error during the period, Day 1 Hydropower Stations In the scenario Storage capacity during the period, Day 1 Hydropower Stations In the scenario Discharge flow during the period.

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