Optimized bidding method for hybrid power supply chain

Abstract

The invention discloses an optimized bidding method for a hybrid power supply chain, and relates to the technical field of electric power market bidding. According to the method, a mixed competitive power supply chain including renewable energy power generation enterprises, fossil fuel power generation enterprises and power retailers is constructed, and four operation modes of centralized decision making, simultaneous bidding, first bidding of the renewable energy power generation enterprises and first bidding of the fossil fuel power generation enterprises are considered. By establishing a demand function and a profit function of each participant, an equilibrium solution in different modes is solved by applying a reverse induction method, and the optimal wholesale electricity price and the optimal electricity purchase quantity are determined. According to the method, internal independent resource autonomous bidding right and aggregator overall collaborative scheduling are deeply combined for the first time, a distributed robust optimization model is fused to process renewable energy uncertainty, unified decision of risk control, market game and internal collaboration is realized, and economic robustness and strategy competitiveness of a hybrid power supply chain are improved.

Description

Technical Field

[0001] This invention relates to the field of electricity market bidding technology, and in particular to an optimized bidding method for a hybrid power supply chain. Background Technology

[0002] In the electricity market sector, existing technologies primarily focus on optimizing bidding strategies to maximize returns for various aggregates (such as Virtual Power Plants (VPPs) and Multi-Energy Producers / Consumers (MEPs)) across multiple electricity markets. These studies typically integrate various resources, including distributed photovoltaic (PV), wind power, energy storage (ES), and flexible loads, to compete in coupled markets such as day-ahead electricity, frequency regulation ancillary services, reserve, and carbon trading. To address the uncertainty of renewable energy output, academia widely employs methods such as Distributed Robust Optimization (DRO), Robust Optimization (RO), and Conditional Value at Risk (CVaR) to seek a balance between pursuing returns and mitigating risks.

[0003] Regarding internal market coordination mechanisms, theories such as master-slave game theory, Nash negotiation, and marginal utility-based bidding have been used to coordinate the distribution and scheduling of benefits among aggregators and their internal diverse stakeholders. However, current research often treats the aggregator as a whole for external bidding or focuses on internal optimization and scheduling, with insufficient exploration of how individual internal resources (especially energy storage) can simultaneously possess the flexibility for independent external bidding and internal collaborative cooperation. Furthermore, research on deeply integrating decentralization optimization with internal and external collaborative bidding decisions is relatively lacking. This results in existing electricity market bidding strategies exhibiting insufficient flexibility and coordination, making it difficult to simultaneously balance "maximizing overall external benefits" and "autonomous decision-making rights of internal stakeholders," thus failing to achieve optimal resource utilization efficiency and economic returns. Summary of the Invention

[0004] This invention aims to address the core issues of insufficient flexibility and lack of coordination in existing electricity market bidding strategies. It provides an optimized bidding method for hybrid power supply chains, enabling each independent resource within the supply chain to maintain a certain degree of independent bidding capability while deeply coordinating with the overall strategy of aggregators. Furthermore, it utilizes advanced methods to uniformly manage market risks, thereby enhancing the competitiveness and overall profitability of the entire hybrid power supply chain in complex and diverse electricity markets.

[0005] The technical solution to achieve the above objectives is: an optimized bidding method for a hybrid power supply chain, comprising the following steps:

[0006] S1. Construct a hybrid competitive electricity supply chain system, which includes renewable energy power generation companies, fossil fuel power generation companies, electricity retailers, government entities providing electricity price subsidies, and four operating models;

[0007] S2, setting relevant assumptions about a hybrid competitive electricity supply chain;

[0008] S3, establish the demand function and the profit functions for renewable energy power generation companies, fossil fuel power generation companies, and electricity retailers;

[0009] S4. For the four operating modes, the game equilibrium solution is obtained by using backward induction to determine the optimal wholesale electricity price and electricity purchase volume for each operating mode:

[0010] S5. Based on the equilibrium solutions and profit results under different modes, and combined with sensitivity analysis, select the optimal bidding strategy.

[0011] The above-mentioned optimized bidding method for a hybrid power supply chain includes four operating modes: centralized decision-making mode, simultaneous bidding mode, renewable energy power generation enterprise bidding mode, and fossil fuel power generation enterprise bidding mode.

[0012] The above-mentioned optimized bidding method for a hybrid power supply chain, specifically step S2, is as follows:

[0013] All participants in the supply chain are risk-neutral, aiming to maximize expected profits, and the game is a game of perfect information.

[0014] The renewable energy power generation satisfies q = yK, where q represents the renewable energy power generation, K is the renewable energy investment capacity, and y is the renewable energy output intensity;

[0015] The electricity market demand satisfies D = xd, where D is the actual electricity market demand, d is the potential maximum demand, and x is the demand intensity factor.

[0016] If power generators and electricity retailers do not engage in energy storage, the losses due to insufficient renewable energy generation will be borne by the renewable energy power generators.

[0017] Both types of electricity are homogeneous products, and the government-guided prices are the same.

[0018] In the above-mentioned optimized bidding method for a hybrid power supply chain, the demand function in step S3 is specifically as follows:

[0019] w i =w 0i -a Qi +bQ 3-i i = 1, 2

[0020] Among them, w 0i This represents the potential maximum wholesale price for electricity supplied by power generation companies. Parameter a is a sensitivity coefficient, indicating how sensitive the wholesale price of electricity offered by power generation companies is to the amount of electricity they purchase from themselves. Parameter b is a competition coefficient, reflecting the degree of competition among power generation companies. Q i Q represents the amount of electricity that retailers purchase from power generation company i. 3-iElectricity purchased by retailers from competing power generation companies.

[0021] In the above-mentioned optimized bidding method for a hybrid power supply chain, in step S3, the profit function of the renewable energy power generation enterprise is:

[0022] π1=w1Q1+(μ-c1)E{min(Q1,yK)}-gE[(Q1-yK) + ]

[0023] Where π1 is the expected profit of renewable energy power generation companies, w1 is the wholesale electricity price of renewable energy power generation companies, Q1 is the electricity purchased by renewable energy power generation companies, μ is the government subsidy for renewable energy grid connection, c1 is the cost of renewable energy grid connection, E is the expected probability, y is the output intensity of renewable energy, K is the investment capacity of renewable energy, and g is the loss cost caused by insufficient renewable energy output.

[0024] In the above-mentioned optimized bidding method for a hybrid power supply chain, in step S3, the profit function of the fossil fuel power generation enterprise is:

[0025] π² = w²Q² - c²Q²

[0026] Where π2 is the expected profit of fossil fuel power generation companies, w2 is the wholesale electricity price of fossil fuel power generation companies, Q2 is the electricity purchased by fossil fuel power generation companies, and c2 is the unit cost of fossil fuel power generation.

[0027] In the above-described optimized bidding method for a hybrid power supply chain, in step S3, the profit function of the power retailer is:

[0028] π r =pE{min(Q1, D1)}+pE{min(Q2, D2)}-w1Q1-w2Q2

[0029] Where, π r is the expected profit of electricity retailers, p is the government-led electricity retail price, D1 is the electricity demand from renewable energy sources, and D2 is the electricity demand from fossil fuels.

[0030] The above-mentioned optimized bidding method for a hybrid power supply chain, specifically step S4 is as follows:

[0031] Centralized decision-making model: Determine the optimal amount of renewable electricity and fossil fuel electricity to be purchased with the goal of maximizing the total profit of the supply chain;

[0032] Simultaneous bidding mode: Power generators, as leaders, submit bids simultaneously, while retailers, as followers, determine the purchase quantity. First, the optimal purchase quantity corresponding to maximizing the retailer's profit is solved, and then the optimal wholesale electricity price is solved by substituting it into the power generator's profit function.

[0033] The renewable energy power generation companies first compete for bids model: retailers determine the purchase volume, renewable energy power generation companies first determine their bids, fossil fuel power generation companies then submit their bids, and finally retailers determine the purchase volume of both renewable energy power generation companies and fossil fuel power generation companies;

[0034] Fossil fuel power generation companies compete for the first bid: retailers determine the purchase volume, fossil fuel power generation companies first determine their bids, renewable energy power generation companies then submit their bids, and finally retailers determine the purchase volume from both renewable energy power generation companies and fossil fuel power generation companies.

[0035] In the above-mentioned optimized bidding method for a hybrid power supply chain, in step S4, under the centralized decision-making model, the total profit of the supply chain is a joint concave function of the purchase volume of renewable electricity and the purchase volume of fossil fuel electricity, and there exists a unique optimal purchase combination that maximizes the total profit. At the same time, under the bidding model, the renewable energy power generation enterprise bidding model, and the fossil fuel power generation enterprise bidding model, the expected profit of the electricity retailer is a joint concave function of the two electricity purchase volumes, and there exists a unique optimal purchase volume and wholesale electricity price under each model.

[0036] In the above-mentioned optimized bidding method for a hybrid power supply chain, step S5 involves analyzing the impact of government electricity price subsidy coefficients on wholesale electricity prices and purchase volume, as well as the impact of competition coefficients on wholesale electricity prices and purchase volume.

[0037] This invention's optimized bidding method for a hybrid power supply chain is the first to systematically and deeply integrate the autonomous bidding rights of independent internal resources with the overall collaborative scheduling of aggregators. While allowing resources such as energy storage to participate independently in parts of the market, it achieves coordinated optimization with the overall supply chain strategy through master-slave game theory or Nash negotiation mechanisms, thus breaking through the flexibility limitations of traditional aggregation models. Technically, by integrating the sub-Bruker optimization model reflecting the uncertainty of renewable energy with the aforementioned collaborative bidding mechanism, a unified decision-making model is constructed. This model can simultaneously optimize bidding volumes in different markets, internal profit distribution, and risk preferences, achieving a "three-in-one" decision-making process of risk control, market game theory, and internal collaboration. This significantly enhances the economic robustness and strategic competitiveness of the hybrid power supply chain in complex coupled market environments. Attached Figure Description

[0038] Figure 1 A schematic diagram of a hybrid competitive supply chain;

[0039] Figure 2 A schematic diagram illustrating the decision-making sequence under a centralized supply chain model;

[0040] Figure 3 A diagram illustrating the decision-making sequence for simultaneous bidding by renewable energy and fossil fuel power generation companies;

[0041] Figure 4 A diagram illustrating the decision-making order for prioritizing bidding by renewable energy power generation companies;

[0042] Figure 5 A diagram illustrating the decision-making sequence for priority bidding by fossil fuel power generation companies;

[0043] Figure 6 A diagram illustrating the impact of government subsidies on wholesale prices;

[0044] Figure 7 A diagram illustrating the impact of government subsidies on electricity procurement volume;

[0045] Figure 8 This is a diagram illustrating the impact of the competition coefficient on wholesale prices.

[0046] Figure 9 This is a diagram illustrating the impact of the competition coefficient on the purchase volume.

[0047] Figure 10 A schematic diagram illustrating optimal decision-making under a centralized supply chain;

[0048] Figure 11 This diagram illustrates the optimal power procurement volume under different bidding strategies. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] This invention provides an optimized bidding method for hybrid power supply chains. First, a power supply chain under an electricity price subsidy policy is constructed. (See attached image.) Figure 1 The supply chain consists of two dominant power generators and one electricity retailer. Electricity price subsidies are provided by the government. The power generators supply electricity to the electricity retailer, who then sells it to end users. In this game theory model, the two power generators first determine the wholesale price at which they sell electricity to the retailer. The retailer, based on the feed-in tariff, determines the quantity of both types of electricity it purchases and sells it to the end consumer at the government-guided price.

[0052] Hybrid electricity supply chains can operate under four different models: centralized decision-making (CS), simultaneous bidding (SP), renewable energy generator bidding first (RP), and fossil fuel generator bidding first (FP). These four models are represented by superscripts CS, SP, RP, and FP, and subscripts 1, 2, and r represent renewable energy generators, fossil fuel generators, and electricity retailers, respectively.

[0053] To simplify the relatively complex real-world scenario and facilitate more effective research, the following assumptions and explanations are made regarding the hybrid power supply chain:

[0054] (1) Electricity Retailer Procurement: The electricity retailer purchases two types of electricity from two power generators, each with its own market demand. It is assumed that all participants in the supply chain are risk-neutral and aim to maximize expected profits. Furthermore, this is a game of perfect information, meaning that each participant has complete knowledge of the information held by the other participants.

[0055] (2) Renewable energy power generation: The power generation of renewable energy depends on the investment capacity. In the case of unstable renewable energy supply, the power generation is expressed as the product of investment capacity and power generation intensity factor, i.e., q = yK, where q represents renewable energy power generation, K is renewable energy investment capacity, and y is renewable energy output intensity. In order to simplify the calculation while maintaining accuracy, it is assumed that y follows a uniform distribution and y ~ U(0,1).

[0056] (3) Uncertain Market Demand: In the newsboy model, uncertain market demand can be represented in two forms: multiplicative and additive. In this case, the multiplicative form is used to represent the uncertain electricity market demand, i.e., D = xd, where D is the actual electricity market demand, d is the potential maximum demand, and x is the demand intensity factor. Similar to the generation intensity factor, it is assumed that x follows a uniform distribution, x ~ U(0,1).

[0057] (4) No energy storage: Since large-scale energy storage technology is still under development, neither power generators nor electricity retailers engage in energy storage. Therefore, the residual value of electricity is not considered. When renewable energy generation is insufficient, the loss is borne by the renewable energy generators, and no additional losses are considered.

[0058] (5) Renewable energy grid connection cost: Due to the inherent instability of renewable energy power generation, the generated electricity needs to be adjusted in terms of voltage, current, and power before being connected to the grid. Therefore, there is a unit grid connection cost for renewable energy power generators, denoted as c1.

[0059] (6) Government subsidies: The government determines the subsidy coefficient for renewable energy power and provides a one-time subsidy to renewable energy power generators based on the actual amount of renewable energy power connected to the grid.

[0060] (7) Homogeneity of Electricity Types: From a physical perspective, the two types of electricity are homogeneous, and demand is influenced by factors such as consumer environmental awareness. Therefore, the government-guided prices for the two types of electricity are the same, denoted as p. A summary and explanation of the relevant parameters are shown in Table 1.

[0061] Table 1, Glossary:

[0062]

[0063]

[0064] Then, the demand function and profit function are established.

[0065] (1) Revised demand function: When electricity retailers place orders, competition arises between renewable energy and conventional fossil fuel power generation. Therefore, the wholesale grid connection price offered by power generation companies can be expressed as a linear function of the amount of electricity purchased from themselves and the amount purchased from their competitors. The function expression is as follows.

[0066] w i =w 0i -aQ i +bQ 3-i i = 1, 2 (1)

[0067] In equation (1), w 0i This represents the potential maximum wholesale electricity price offered by power generation companies. Parameter 'a' is a sensitivity coefficient, indicating how sensitive the wholesale electricity price of a power generation company is to the amount of electricity it purchases from itself. Parameter 'b' is a competition coefficient, reflecting the degree of competition among power generation companies. Generally, the wholesale electricity price of a power generation company is more sensitive to the amount of electricity retailers expect to purchase from itself than to the amount they expect to purchase from its competitors. Therefore, in this case, we assume a > b > 0.

[0068] (2) Profit function of renewable energy power generation companies: Considering the uncertainty of renewable energy power generation and government electricity price subsidies, and assuming no residual value or other income loss, the expected profit function of renewable energy power generation companies can be expressed as follows:

[0069] π1=w1Q1+(μ-c1)E{min(Q1,yK)}-gE[(Q1-yK) + (2)

[0070] In equation (2), π1 is the expected profit of renewable energy power generation enterprises, w1 is the wholesale electricity price of renewable energy power generation enterprises, Q1 is the electricity purchased by renewable energy power generation enterprises, μ is the government subsidy for renewable energy grid connection, c1 is the grid connection cost of renewable energy, W is the probability expectation, y is the output intensity of renewable energy, K is the investment capacity of renewable energy, and g is the loss cost caused by insufficient renewable energy output. The first term w1Q1 represents the electricity sales revenue of renewable energy power generation enterprises. Since the government subsidy is based on the actual consumption of renewable energy, the second term (μ-c1)E{min(Q1, yK)} reflects the comprehensive term of electricity cost. The last term gE[(Q1-yK)] + This indicates the losses borne by power generation companies when there is a shortage of renewable energy power supply.

[0071] (3) Profit function of fossil fuel power generation companies: Compared with renewable energy power generation, fossil fuel power generation can provide a stable and reliable power output, and therefore there is no power shortage problem. The expected profit function of fossil fuel power generation companies is expressed as follows:

[0072] π² = w²Q² - c²Q² (3)

[0073] In equation (3), π2 is the expected profit of the fossil fuel power generation company, w2 is the wholesale electricity price of the fossil fuel power generation company, Q2 is the electricity purchased by the fossil fuel power generation company, and c2 is the unit cost of fossil fuel power generation. The first term, w2Q2, represents the electricity sales revenue of the fossil fuel power generation company. Since fossil fuel power generation requires the combustion of a large amount of fossil fuel, the second term, c2Q2, represents the fossil fuel consumption cost.

[0074] (4) Profit function of electricity retailers: Electricity retailers sell the electricity they purchase to the electricity consumption market. Considering the uncertainty of market demand, the expected profit function of electricity retailers can be expressed as follows.

[0075] π r =pE{min(Q1,D1)}+pE{min(Q2,D2)}-w1Q1-w2Q2 (4)

[0076] In equation (4), π r Let p be the expected profit of the electricity retailer, p be the government-led electricity retail price, D1 be the electricity demand for renewable energy, and D2 be the electricity demand for fossil fuels. The first term, pE{min(Q1, D1)}, represents the revenue of the electricity retailer from selling renewable electricity, and the second term, pE{min(Q2, D2)}, represents the revenue of the electricity retailer from selling fossil fuel electricity. The third term, w1Q1, represents the cost of the retailer purchasing renewable electricity, and the fourth term, w2Q2, represents the cost of the retailer purchasing fossil fuel electricity.

[0077] Please see Figure 2 In centralized supply chains, power generators (both renewable and fossil fuel generators) and electricity retailers are integrated into a single entity. Therefore, the wholesale feed-in price at the intermediate stage is no longer a decision variable. Instead, the electricity retailer simultaneously determines the purchase volume Q1. CS and Q2 CS The decision-making process is as follows: Figure 2 As shown.

[0078] Please see Figure 3 Typically, renewable energy power generation companies and fossil fuel power generation companies simultaneously determine their wholesale electricity prices. In many electricity markets across the world, renewable energy power generation companies and fossil fuel power generation companies (such as coal-fired power plants and natural gas power plants) often submit bids simultaneously and compete in the market together. The order of decision-making events is as follows: Figure 3 As shown.

[0079] However, due to various influencing factors, there may be instances where one party delays in submitting a bid. Therefore, this section considers two additional scenarios: one where renewable energy power generation companies have priority in submitting bids, and the other where fossil fuel power generation companies have priority in submitting bids.

[0080] Please see Figure 4 In the construction of the electricity spot market, a multi-stage centralized bidding mechanism was adopted, giving renewable energy power generation companies priority in market entry and a competitive advantage. The decision-making order for the priority bidding scenario for renewable energy power generation companies is as follows: Figure 4 As shown.

[0081] Please see Figure 5 Furthermore, in the real electricity market, fossil fuel power generation companies may also participate in priority bidding or submit electricity orders in advance to increase their chances of market clearing and improve potential profits. Figure 5 This demonstrates the decision-making process in a scenario where fossil fuel power generation companies gain priority in bidding.

[0082] We use backward induction to solve game theory models under four typical scenarios. We first give the profit functions of each participant in the hybrid power supply chain under these four scenarios, and then derive the equilibrium solution for each model.

[0083] Under renewable energy price subsidies, consider a centralized decision-making scenario in a hybrid competitive power supply supply chain, where all participants collaborate on joint decisions. In this scenario, each participant aims to maximize the overall profit of the supply chain. The total profit of the supply chain is defined as electricity sales revenue minus supply costs and power shortage losses. Under centralized decision-making, the grid-connected wholesale price becomes an endogenous variable and no longer requires explicit decision-making. For clarity, the superscript "CS" is used to denote the centralized scenario under the subsidy policy. Therefore, the expected profit function of the power supply supply chain under centralized decision-making can be expressed as follows:

[0084]

[0085] In a centralized supply chain, the total expected profit π of the supply chain total CS It concerns the purchase volume of renewable electricity in Q1. CS Q2 fossil fuel power purchases CS The joint concave function. There exists a pair of optimal purchase quantities. and This makes the total expected profit Maximize. The optimal purchase quantity is given by the following formula:

[0086]

[0087] Against the backdrop of a new round of power market reforms promoting market-oriented mechanisms such as the separation of power generation and grid operation, and the separation of transmission and distribution, the power supply chain has transformed into a decentralized decision-making structure. In this context, power generators and retailers no longer aim to maximize the overall profit of the supply chain. Instead, each participant attempts to optimize its own profits, leading to a non-cooperative game-theoretic relationship between power generators and retailers.

[0088] This decentralized structure essentially constitutes a Stackelberg game, where power generators act as leaders and distributors as followers. Furthermore, due to the difference in bidding order among power generators, there is also strategic interaction between renewable energy power generators and fossil fuel power generators. First, we analyze the scenario where both types of power generators submit bids simultaneously, denoted by the superscript "SP". Under decentralized decision-making, the expected profit function of the distributor is as follows:

[0089]

[0090] The optimal profit for electricity retailers is a joint concave function of the amount of renewable and fossil fuel electricity purchased. This is under the condition... When satisfied, there exists a way to increase the retailer's profit. Maximize the optimal purchase quantity and The optimal purchase quantity is given by the following formula:

[0091] Based on the principle of backward induction, substituting the optimal purchase quantity of the two types of electricity by the retailer into the profit function of the renewable electricity producer yields the following expression.

[0092]

[0093] Continuing the analysis of fossil fuel power generation companies' decisions, by substituting the optimal purchase quantities of the two types of electricity by retailers into the profit function of fossil fuel power generation companies, we can obtain the following expression.

[0094]

[0095] The optimal profit for renewable energy power generation companies is A continuously concave function. When the condition... There exists an optimal value when this condition is met.

[0096]

[0097] This allows renewable energy power generation companies to maximize their profits. Similarly, the optimal profit for fossil fuel power generation companies is... A continuously concave function. When the condition... When satisfied, there exists an optimal value. It can maximize the profits of fossil fuel power generation companies.

[0098] Under decentralized decision-making, when renewable energy power generation companies have priority in bidding, the strategic interactions among them essentially form a Stackelberg game dominated by renewable energy power generation companies. In this case, the supply chain is denoted by the superscript "RP". This supply chain can be modeled as a three-level structure. According to the principle of backward induction, the solution process proceeds in the following order: the electricity retailer first determines the purchase quantities of the two types of electricity, and Then, fossil fuel power plants determine their potential maximum wholesale grid connection price. Finally, renewable energy power generation companies determine their potential maximum wholesale grid connection price.

[0099] By combining the profit functions of electricity retailers and renewable energy power generation companies, the optimal purchase quantity for retailers can be derived under the scenario where renewable energy power generation companies have priority in bidding. and and the optimal grid connection wholesale price for fossil fuel power generation These are given by the following formula:

[0100] ,where S3=-ap+(a 2 -b2 )d2; and

[0101] Under decentralized decision-making, when fossil fuel power generation companies offer preferential pricing, the strategic interactions among these companies transform into a Stackelberg game dominated by fossil fuel power generation companies. The supply chain in this scenario is denoted by the superscript "FP". The solution sequence is as follows: The electricity retailer first determines the purchase quantities of the two types of electricity. and Then renewable energy power generation companies set their grid connection wholesale prices. Finally, fossil fuel power plants determine their grid connection wholesale prices.

[0102] Therefore, under the scenario where fossil fuel power generation companies give priority to bidding, the optimal purchase quantity for electricity retailers is... And the optimal grid connection wholesale price set by renewable energy power generation companies as follows:

[0103]

[0104] Under the scenario where fossil fuel power generation companies are given priority in participating in the bidding, their optimal profit is A continuous concave function. When the condition is satisfied... At that time, there exists an optimal value. It can maximize the profits of fossil fuel power generation companies.

[0105] To obtain intuitive and observable results, numerical analysis was conducted based on the previously established theoretical model. The analysis mainly included profit assessment and sensitivity analysis of key parameters. The following parameter values ​​were used in the numerical experiments: the government-guided retail electricity price was set at p = 0.5; the consumption cost of renewable electricity was c1 = 0.15; the cost of fossil fuel power generation was c2 = 0.25; the cost of losses due to insufficient renewable electricity supply was g = 0.7; and the government subsidy for renewable electricity consumption was μ = 0.15. The installed capacity of renewable energy was [value missing], the maximum potential market demand for renewable electricity was d1 = 5000, and the maximum potential market demand for fossil fuel electricity was d2 = 5000.

[0106] This invention focuses on game-based decision-making processes in a hybrid competitive electricity supply chain under price subsidy policies. In this context, two key parameters are the government electricity price subsidy μ and the competition coefficient b, the latter reflecting the intensity of competition between renewable energy producers and fossil fuel power producers. Therefore, the following analysis will also explore how these two parameters affect the decisions and profits of producers, electricity retailers, and the entire supply chain.

[0107] In the sensitivity analysis of government electricity price subsidies μ, all other parameters were kept constant, and the subsidy range was set to μ∈(0,0.3). This analysis primarily examines how changes in subsidy levels affect the grid-connected wholesale prices of renewable and fossil fuel power producers. Simulations were performed in MATLAB, and the results are as follows: Figure 6 As shown.

[0108] like Figure 6 As shown, when other parameters remain constant, an increase in the government subsidy coefficient μ leads to a sustained decrease in the wholesale grid-connected price of renewable energy power in all three bidding scenarios. The price decrease is most pronounced in the simultaneous bidding model. This is because the subsidy directly targets renewable energy power generation companies, incentivizing them to lower the wholesale grid-connected price.

[0109] In contrast, fossil fuel power generation companies do not receive direct financial support from subsidies. However, the graph clearly shows that as subsidy levels increase, their wholesale grid connection prices also tend to decline. This result is mainly driven by increasingly fierce competition between the two power generation methods. The results indicate that even without directly intervening in fossil fuel power generation pricing, the subsidy mechanism can effectively curb excessive pricing behavior in the hybrid electricity market by promoting competition.

[0110] The fundamental goal of government subsidies for renewable energy generation is to increase the share of renewable energy in the electricity market. Figure 7 It demonstrates how changes in the subsidy factor μ affect electricity retailers' purchasing of these two types of electricity.

[0111] like Figure 7 As shown, when other parameters remain constant, an increase in the government subsidy coefficient μ leads to a sustained increase in the purchase of renewable electricity by electricity retailers across all three bidding scenarios, while a decrease in the purchase of fossil fuel electricity. This indicates that the electricity price subsidy mechanism effectively promotes the consumption of renewable electricity.

[0112] Furthermore, the different trends in the two electricity purchase volumes reflect the increasing competitive advantage of renewable electricity as subsidies increase. This competitive advantage explains why retailers have correspondingly reduced their purchases of fossil fuel electricity, as renewable electricity becomes more cost-effective, thus driving a shift in purchasing preferences.

[0113] The competition coefficient *b* reflects the intensity of competition between renewable energy producers and fossil fuel energy producers. With the deepening of electricity market reforms and the gradual improvement of market mechanisms, competition between these two types of producers is becoming increasingly fierce. As mentioned earlier, the feasible range for the competition coefficient in this model is *b* ∈ (0, 0.00005). Therefore, when conducting sensitivity analysis on *b*, all other parameters are kept constant, and the competition coefficient is varied within the range of *b* ∈ (0, 0.00005). The following... Figure 8 The simulation results demonstrate how the competition coefficient affects the online wholesale prices of two types of producers.

[0114] like Figure 8 As shown, when all other parameters remain constant, in all three bidding scenarios, the grid-connected wholesale prices for both renewable energy and fossil fuel power generation companies tend to decrease as the competition coefficient b increases. The price decrease is fastest in the simultaneous bidding scenario, and slowest in the scenario that prioritizes fossil fuel power generation companies.

[0115] This trend can be explained by the fact that the higher the competition coefficient b, the more intense the strategic interaction between the two power generation companies. As competition intensifies, each power generation company becomes more sensitive to the pricing behavior of its competitors and tends to lower its own prices to maintain or expand its market share. In a simultaneous bidding scenario, both power generation companies adjust their prices simultaneously, maximizing competitive pressure and resulting in the most significant price reductions for both types of electricity. In contrast, when fossil fuel power generation companies are given priority in bidding, they set their prices first, without knowing the exact pricing reactions of renewable energy power generation companies. To avoid overreacting or losing profit margins, fossil fuel power generation companies tend to adopt more conservative pricing strategies, resulting in slower price reductions. Meanwhile, renewable energy power generation companies, as followers, can only respond passively, further limiting the overall adjustment speed. This sequential dynamic reduces the intensity of mutual pricing pressure, resulting in the slowest wholesale price reductions for both power generation companies under this bidding structure.

[0116] Similarly, keeping all other parameters constant, the competition coefficient was set to a range of b∈(0,0.00005), and the impact of the competition coefficient on the purchase volume of electricity retailers was analyzed. The simulation results are as follows: Figure 9 As shown.

[0117] like Figure 9 As shown, since the competition coefficient b only appears in the expression for the wholesale price w, and the centralized decision-making model does not involve wholesale price decisions, the electricity purchase quantity Q remains unchanged under the centralized decision-making (CS) scenario.

[0118] Different purchasing trends were observed under three decentralized bidding scenarios. When two power generators bid simultaneously, as b increases, retailers' purchases of renewable energy and fossil fuel power initially decrease and then increase. When renewable energy power generators are prioritized, as b increases, the purchase volume of renewable energy power continues to decrease, while the purchase volume of fossil fuel power still follows a pattern of decreasing first and then increasing. Conversely, when fossil fuel power generators are prioritized, retailers' purchase volume of renewable energy power exhibits a similar trend of decreasing first and then increasing, while the purchase volume of fossil fuel power continues to decrease.

[0119] These patterns can be interpreted as follows: a larger competition coefficient b intensifies the price competition among power generators, leading to dynamic adjustment of the wholesale price. In the case of simultaneous bidding, both power generators face the same downward price pressure, and the initial pricing uncertainty prompts retailers to reduce purchases before price stabilization and then increase purchases. In the scenario of priority bidding, the leading power generator has more pricing power and tends to set more competitive prices, while the follower responds passively. This imbalance causes retailers to reallocate their purchase volumes based on relative price competitiveness, resulting in a continuous decline in the purchase volume of one type of electricity and a turning point for the other. Therefore, the intensity of competition not only affects the pricing outcome but also affects the purchase behavior and structure of retailers.

[0120] Numerical analysis shows that in the centralized decision-making scenario, the expected profit of the power supply chain is a joint concave function of the procurement volume of renewable energy electricity and the procurement volume of fossil fuel electricity. There exists a unique optimal procurement portfolio that maximizes the total expected profit of the supply chain, as Figure 10 shown. Specifically, the optimal procurement volume is and the maximum total expected profit of the supply chain reaches 937.5.

[0121] Under decentralized decision-making, power generators and electricity retailers form a Stackelberg game, where the on-grid wholesale price directly affects the profits of all participants. For power generators, the wholesale price is not only affected by the procurement volume from the retailer itself but also by the procurement volume from competing power generators. To describe this situation, the sensitivity coefficient a and the competition coefficient b were defined previously.

[0122] According to the constraints derived in the previous section, the sensitivity coefficient should be within the interval a ∈ (0, 0.00005). Without loss of generality, the present invention sets a = 0.00003. Given the assumption b < a, the competition coefficient must satisfy b ∈ (0, 0.00005), so we accordingly set b = 0.00002.

[0123] With these parameter values, the optimal decisions and maximum expected profits of each participant under different bidding orders in a decentralized decision-making environment can be determined. The relationship between the expected profit of the retailer and its procurement volume is as Figure 11 shown.

[0124] As Figure 11As shown, under all three bidding orders, the retailer's expected profit is a joint concave function of the renewable electricity purchase quantity and the fossil fuel electricity purchase quantity. This means that in each case, there exists a unique set of optimal purchase quantities that maximize the retailer's expected profit. Specifically, when renewable electricity suppliers and fossil fuel electricity suppliers bid simultaneously, the optimal purchase quantity is... The retailer's maximum expected profit is 295.86. When the renewable energy supplier submits its bid first, the optimal purchase quantity is... The retailer's maximum expected profit is 276.80. When fossil fuel power suppliers submit their bids first, the optimal purchase quantity is... The retailer's maximum expected profit is 264.46.

[0125] Table 2 below clearly summarizes the decision-making and profit outcomes of each participant in a hybrid competitive power supply supply chain under four different scenarios.

[0126] Table 2. Equilibrium results under four different scenarios:

[0127]

[0128] Based on the results presented in Table 2, the following conclusions can be drawn. Under centralized decision-making, the total expected profit of the power supply supply chain reaches its maximum, and both renewable and fossil fuel power can be integrated to the greatest extent. In contrast, under decentralized decision-making, although the purchase volume of power retailers varies slightly under different bidding orders, the overall difference in total expected profit across the three scenarios is relatively small, and the expected profit fluctuations of each supply chain member are not significant. This consistency further confirms the robustness and reliability of the proposed model.

[0129] Among the three decentralized bidding scenarios, the total profit of the power supply supply chain is highest in the simultaneous bidding mode, followed by the mode where renewable energy power generation companies have priority bidding, while the total profit is lowest when fossil fuel power generation companies have priority bidding. In terms of individual revenue, renewable energy power generation companies have the highest profit when bidding first, fossil fuel power generation companies also profit the most, while electricity retailers have the highest profit in the simultaneous bidding scenario. Furthermore, regardless of the bidding mode, the grid connection wholesale prices of renewable energy and fossil fuel electricity remain equal in equilibrium.

[0130] In summary, the optimized bidding method for the hybrid power supply chain of this invention is the first to deeply integrate the independent bidding rights of internal resources with the overall collaborative scheduling of aggregators, and incorporates a distributed robust optimization model to handle the uncertainty of renewable energy, thereby achieving unified decision-making for risk control, market competition and internal collaboration, and improving the economic robustness and strategic competitiveness of the hybrid power supply chain.

[0131] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0132] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An optimized bidding method for a hybrid power supply chain, characterized in that, Includes the following steps: S1. Construct a hybrid competitive electricity supply chain system, which includes renewable energy power generation companies, fossil fuel power generation companies, electricity retailers, government entities providing electricity price subsidies, and four operating models; S2, setting relevant assumptions about a hybrid competitive electricity supply chain; S3, establish the demand function and the profit functions for renewable energy power generation companies, fossil fuel power generation companies, and electricity retailers; S4. For the four operating modes, the game equilibrium solution is obtained by using backward induction to determine the optimal wholesale electricity price and electricity purchase volume for each operating mode: S5. Based on the equilibrium solutions and profit results under different modes, and combined with sensitivity analysis, select the optimal bidding strategy.

2. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, The four operating models are the centralized decision-making model, the simultaneous bidding model, the renewable energy power generation enterprise bidding model, and the fossil fuel power generation enterprise bidding model.

3. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, Step S2 is as follows: All participants in the supply chain are risk-neutral, aiming to maximize expected profits, and the game is a game of perfect information. The renewable energy power generation satisfies q = yK, where q represents the renewable energy power generation, K is the renewable energy investment capacity, and y is the renewable energy output intensity; The electricity market demand satisfies D = xd, where D is the actual electricity market demand, d is the potential maximum demand, and x is the demand intensity factor. If power generators and electricity retailers do not engage in energy storage, the losses due to insufficient renewable energy generation will be borne by the renewable energy power generators. Both types of electricity are homogeneous products, and the government-guided prices are the same.

4. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, In step S3, the demand function is specifically as follows: w i =w 0i -aQ i +bQ 3-i ,i=1,2 Among them, w 0i This represents the potential maximum wholesale price for electricity supplied by power generation companies. Parameter a is a sensitivity coefficient, indicating how sensitive the wholesale price of electricity offered by power generation companies is to the amount of electricity they purchase from themselves. Parameter b is a competition coefficient, reflecting the degree of competition among power generation companies. Q i Q represents the amount of electricity that retailers purchase from power generation company i. 3-i Electricity purchased by retailers from competing power generation companies.

5. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, In step S3, the profit function of the renewable energy power generation enterprise is: π1=w1Q1+(μ-c1)E{min(Q1,yK)}-gE[(Q1-yK) + ] Where π1 is the expected profit of renewable energy power generation companies, w1 is the wholesale electricity price of renewable energy power generation companies, Q1 is the electricity purchased by renewable energy power generation companies, μ is the government subsidy for renewable energy grid connection, c1 is the cost of renewable energy grid connection, E is the expected probability, y is the output intensity of renewable energy, K is the investment capacity of renewable energy, and g is the loss cost caused by insufficient renewable energy output.

6. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, In step S3, the profit function of the fossil fuel power generation enterprise is: π² = w²Q² - c²Q² Where π2 is the expected profit of fossil fuel power generation companies, w2 is the wholesale electricity price of fossil fuel power generation companies, Q2 is the electricity purchased by fossil fuel power generation companies, and c2 is the unit cost of fossil fuel power generation.

7. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, In step S3, the profit function of the electricity retailer is: π r [pE{min(Q1,D1)}+pE{min(Q2,D2)}-w1Q1-w2Q2 Where, π r is the expected profit of electricity retailers, p is the government-led electricity retail price, D1 is the electricity demand from renewable energy sources, and D2 is the electricity demand from fossil fuels.

8. The optimized bidding method for a hybrid power supply chain according to claim 2, characterized in that, Step S4 is as follows: Centralized decision-making model: Determine the optimal amount of renewable electricity and fossil fuel electricity to be purchased with the goal of maximizing the total profit of the supply chain; Simultaneous bidding mode: Power generators, as leaders, submit bids simultaneously, while retailers, as followers, determine the purchase quantity. First, the optimal purchase quantity corresponding to maximizing the retailer's profit is solved, and then the optimal wholesale electricity price is solved by substituting it into the power generator's profit function. The renewable energy power generation companies first compete for bids model: retailers determine the purchase volume, renewable energy power generation companies first determine their bids, fossil fuel power generation companies then submit their bids, and finally retailers determine the purchase volume of both renewable energy power generation companies and fossil fuel power generation companies; Fossil fuel power generation companies compete for the first bid: retailers determine the purchase volume, fossil fuel power generation companies first determine their bids, renewable energy power generation companies then submit their bids, and finally retailers determine the purchase volume from both renewable energy power generation companies and fossil fuel power generation companies.

9. The optimized bidding method for a hybrid power supply chain according to claim 8, characterized in that, In step S4, under the centralized decision-making model, the total profit of the supply chain is a joint concave function of the purchase volume of renewable electricity and the purchase volume of fossil fuel electricity, and there exists a unique optimal purchase combination that maximizes the total profit. At the same time, under the bidding model, the renewable energy power generation enterprise bidding model, and the fossil fuel power generation enterprise bidding model, the expected profit of the electricity retailer is a joint concave function of the two electricity purchase volumes, and there exists a unique optimal purchase volume and wholesale electricity price under each model.

10. The optimized bidding method for a hybrid power supply chain according to claim 1, characterized in that, In step S5, the sensitivity analysis is to analyze the impact of the government electricity price subsidy coefficient on the wholesale electricity price and purchase volume, as well as the impact of the competition coefficient on the wholesale electricity price and purchase volume.

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