Distribution network distributed clean energy bearing optimization method based on market mechanism

Abstract

The invention discloses a market mechanism-based power distribution network distributed clean energy bearing optimization method. The method comprises the following steps of 1, constructing a planning decision model of a power distribution network distributed energy system and a small gas turbine; step 2, constructing a power purchase decision optimization model of the power purchase side power distribution network so as to obtain a decision scheme with the minimum total power purchase cost; and step 3, on the basis of the bidding strategy game model of the distributed power generation unit, constructing a market clearing model of the power transaction center, and obtaining an optimal decision scheme under the model. According to the method, the defect that existing power distribution network planning is difficult to consider clean energy bearing capacity, investment uncertainty and cost optimization is overcome, low-cost power purchase and efficient clean energy consumption are realized, the reliability and flexibility of the system are improved, and the bearing capacity and operation economy of the power distribution network are effectively enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system and energy management technology, and in particular to distribution network planning and operation optimization technology, specifically to a market-based method for optimizing the carrying capacity of distributed clean energy in distribution networks. Background Technology

[0002] With the advancement of global energy transition and the "dual carbon" goal, the penetration rate of clean energy sources such as distributed photovoltaics, wind power, fuel cells, and energy storage in power distribution networks is continuously increasing. The rapid integration of these clean energy sources has promoted the green and low-carbon development of the power system, but it has also significantly increased the uncertainty and complexity of power distribution network operation. On the one hand, distributed energy is intermittent and volatile, easily causing voltage fluctuations and power flow reversals in the distribution network, affecting the safe and stable operation of the system. On the other hand, the electricity demand on the purchasing side in a diversified market environment and the optimization of external power source purchase costs have also placed higher demands on the latter. Against this backdrop, the assessment and optimization of clean energy carrying capacity, as a crucial link in distribution network planning and operation, is facing new challenges. Traditional planning methods struggle to simultaneously consider the absorption capacity of distributed energy, investment risk control, and effective integration with market mechanisms; therefore, there is an urgent need to propose optimization methods adapted to the market environment.

[0003] To improve the absorption of clean energy, reduce electricity purchase costs, and enhance system reliability, an optimization strategy that combines overall planning with market mechanisms is essential. Based on the operational characteristics and energy allocation needs of distribution networks, existing research largely breaks down the problem into different stages and addresses them separately. For example, in the planning stage, the focus is usually on distributed energy capacity allocation or energy storage optimization; in the electricity purchase optimization stage, the emphasis is on reducing electricity procurement costs or rationally allocating the proportion of external electricity purchases; and in the market trading stage, the game process between distributed generation units and the electricity purchaser is studied by constructing bidding strategy models or game frameworks. While this "stage-separated" approach can solve some problems, the lack of an overall systemic design and the failure to reflect the coupling relationships between different stages limit the efficient utilization of clean energy in distribution networks.

[0004] Paper 10.3389 / fenrg.2024.1476691 proposes a distributed energy trading model based on a value allocation mechanism. It utilizes Nash negotiations to coordinate the interests of the generation and purchase sides and employs stochastic programming to handle uncertainty. While innovative in its trading mechanism, this model primarily focuses on revenue distribution and fails to integrate capacity planning, power purchase optimization, and market clearing into a unified framework. Furthermore, paper 10.1109 / TSG.2021.3109130 proposes incorporating Dynamic Line Ratings (DLR) into the optimal planning of distributed energy resources to improve line utilization and reduce investment costs using meteorological information. This method optimizes network capacity at the physical level but lacks collaborative modeling with market mechanisms, failing to simultaneously consider the economic viability of the power purchase side and the market-oriented utilization of distributed energy. Summary of the Invention

[0005] The purpose of this invention is to address the technical problems mentioned in the background art, namely, that current research in existing technical literature cannot coordinate distributed energy planning, power purchase decisions and market clearing, and it is difficult to balance the improvement of the clean energy carrying capacity of the distribution network, investment risk management and market interest coordination. Therefore, this invention provides a market-based method for optimizing the carrying capacity of distributed clean energy in the distribution network.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A market-based optimization method for distributed clean energy carrying capacity in power distribution networks includes the following steps: Step 1: Construct a planning and decision-making model for the distributed energy system of the power distribution network, namely the distributed energy system and the small gas turbine; Step 2: Based on the market-oriented operation mode of the power purchase side distribution network realizing external power purchase through the upper-level power grid, and combined with the seasonal fluctuation characteristics of distributed new energy output, construct a power purchase decision optimization model for the power purchase side distribution network to obtain the decision scheme with the minimum total power purchase cost; Step 3: Based on the bidding strategy game model of distributed generation units, construct a market clearing model for the power trading center and derive the optimal decision scheme under this model; In step 3, the distributed generation unit includes a distributed energy system and a small gas turbine.

[0007] Step 1 includes the following sub-steps: Sub-step 1-1: For distributed energy systems, with the optimization objective of maximizing the total revenue of the planned project, conduct collaborative planning for the capacity of distributed energy sources such as photovoltaics and wind power, as well as multi-timescale energy storage, and then calculate the objective function; Sub-steps 1-2: Using the Black-Scholes option pricing model, combined with the unique characteristics of energy sector investment, to evaluate the value of uncertain options in the investment process of distributed energy system projects; Sub-steps 1-3: To ensure the economy and reliability of distributed energy system planning, and to meet the load demand of the power purchase side distribution network, a series of constraints related to distributed energy system planning are established. Sub-steps 1-4: Focusing on small gas turbines, with the goal of maximizing the total return on investment projects, conduct capacity planning and construct the corresponding objective function.

[0008] In sub-step 1-1, decision variables (including unit capacity and investment scale) and operational strategy variables (including power generation dispatch scheme and unit start-up and shutdown status) are introduced during the planning phase; combined with electricity price parameters for typical seasons, an objective model is constructed with maximizing the total revenue of the distributed energy system as its core: ; in, Let the electricity sales revenue function be the relationship between the electricity sales revenue function and the decision variables. , and clearing electricity prices Correlation, used to reflect market returns; The total cost is related to the planning variables. Related, covering investment costs Operation and maintenance costs fuel costs ; This refers to the maximum total revenue function of a distributed energy system; This refers to net present value; This refers to the value of options for investing in uncertainties; Given the need to improve the reliability of power supply in distributed energy systems, small gas turbines are typically configured. Therefore, the fuel cost of distributed energy systems mainly comes from these small gas turbines, and the fuel cost calculation formula is as follows: ; Where N is the planning period; S is the typical season set; The number of days in a typical season; r The discount rate is N; the planning period is N years. denoted as fuel price; a, b, and c are the fuel consumption coefficients corresponding to small gas turbines.

[0009] In sub-steps 1-2, the obtained model is: ; in, It is the standard normal probability distribution function; The risk-free rate of return on equipment investment; The volatility of asset value for equipment investment. , For parameters related to project benefits and costs, , This is an intermediate variable used to calculate the option value.

[0010] In sub-steps 1-3, when establishing the correlation relationships and supplementing the constraints of the distributed energy system model, the specific steps include: 1) The power balance constraint condition of the transmission system is: ; in, Active power output for various types of power supplies; For the active power output of a small gas turbine; for t The system always has a constant active power load requirement; Power consumption for small gas turbines and other power sources; This refers to the active power output of the photovoltaic power source. This refers to the active power output of the wind power source. This refers to the number of small gas turbines installed. The number of power-consuming devices such as small gas turbines; This refers to the number of photovoltaic power supply units connected to the grid. This represents the number of wind power units connected to the grid.

[0011] 2) The power transmission constraints of the distributed energy system are: ; in, , Minimum and maximum transmission power; 3) Investment constraints are as follows: ; in, , Minimum and maximum allowable capacity; 4) The operating constraints for photovoltaic and wind power output are as follows: ; ; in, , For active power output from photovoltaic and wind power; , The minimum allowable active power for photovoltaic power and the maximum allowable active power for wind power are specified. 5) The operating constraints for small gas turbines are: ; in, , The minimum and maximum output power of a small gas turbine; For small gas turbines, the gradeability is... 6) The operating constraints of the hydrogen storage system are: ; ; ; ; ; ; in: This is the lower heating value of hydrogen. The charging efficiency of the electrolyzer represents the efficiency with which the electrolyzer converts electrical energy into hydrogen. , To improve the efficiency of charging and discharging hydrogen storage tanks; The discharge efficiency of a fuel cell represents the efficiency with which the fuel cell converts hydrogen into electrical energy. for s season t The hydrogen capacity in the hydrogen storage tank at any given time; for s season t The charging power of the electrolytic cell at all times; for s season t The discharge power of the fuel cell at any given time; , They are respectively s and s -1 Hydrogen capacity in the hydrogen storage tank at the start of the season; for s -1 Hydrogen capacity in the storage tank at the end of the season; For the season s The number of days; This is the maximum capacity of the hydrogen storage tank; , These are the maximum electrolysis power of the electrolyzer and the output power of the fuel cell, respectively. and A binary variable representing the operating state of the electrolyzer and the fuel cell; , These represent the hydrogen capacity in the hydrogen storage tanks at the beginning of the first season and the end of the last season, respectively. 7) The constraint model for the battery energy storage system is: ; ; ; ; in: , They are respectively t and t The amount of energy stored in the battery at time -1; , They are respectively t The charging or discharging power at any given time; , These represent the charge and discharge efficiencies of battery energy storage; This represents the maximum energy storage capacity of the battery. , These are binary variables representing the charging and discharging operating states, respectively. and These represent the battery's charge levels at the beginning and end of the day, respectively.

[0012] In sub-steps 1-4, the set of decision variables for the planning phase is introduced. J Decision variables in the planning stage With the set of decision variables during the operation phase Decision variables during the operation phase We construct a cost-relevant function for electricity sales revenue and simultaneously perform hierarchical optimization on the objective function of total revenue from small gas turbines, specifically including a single-layer optimization (an objective function model aimed at maximizing revenue): ; Optimized returns from two-tier real options: ; And construct a method for calculating carbon emission costs: ; in, The actual carbon emissions per unit of electricity generated; Carbon quotas for small gas turbines; The price per unit of carbon emission rights; This is the benchmark value for power generation; This is the unit's peak-shaving correction factor.

[0013] In step 2, the specific sub-steps for constructing the power purchase decision optimization model for the power purchase side distribution network are as follows: Based on the specific power purchase plan, construct a system The matrix, with "minimizing the total load cost of the power purchase distribution network" as its core, serves as the guiding principle for decision-making. It calculates the power procurement cost and grid access fee separately, constructing corresponding mathematical expressions in stages: ; ; in, The cost of purchasing electricity for the power distribution network load on the purchasing side; The network access fees paid for the power distribution network load on the purchasing side; The fee for electricity transmitted over the network per unit of electricity; Construct a total cost model and correlate it with decision-making, establish the correlation between electricity consumption decisions and cost optimization, and output the electricity purchase combination that minimizes the total electricity purchase cost: .

[0014] Step 3 includes the following sub-steps: Sub-step 3-1: Construct a game mechanism for the bidding strategy of distributed generation units to ensure that, in the power market environment, distributed generation units can formulate electricity price and electricity bidding strategies that maximize their own revenue, while the power purchase side distribution network can minimize the power purchase cost. Sub-step 3-2: Based on the bidding situation of distributed generation units, generation plans and the actual demand of the power grid, determine the final clearing price and power trading quota of the power market, and then construct the market clearing model of the power trading center; Sub-step 3-3: Prove that a Nash equilibrium exists in the non-cooperative game involving small gas turbines, and verify that the Nash equilibrium is unique and belongs to the optimal strategy.

[0015] In sub-step 3-1, a game model for the bidding strategy of distributed generation units is constructed; the game participants and strategy sets are defined, and the set of distributed generation units is denoted as . Each distributed generation unit Participation in the game; definition This represents the strategy combinations of each distributed generation unit participating in the game. In addition to distributed generation units k Pricing strategies for other distributed generation units, In addition to distributed generation units k Electricity reporting strategies for distributed generation units other than those mentioned above; The criteria for determining the "optimal strategy" are: for any distributed generation unit... If a distributed generation unit's strategy Strategy combination for other distributed generation units All of these are optimal strategies, and this conclusion applies to any... If all conditions are met, the game reaches equilibrium, where the payoffs and costs for all participants are optimal. The specific mathematical expression is as follows: ; in: , To achieve a clearing price and quantity in the game to reach equilibrium; , These are the game convergence thresholds for distributed generation units and the power purchase side distribution network, respectively.

[0016] In sub-step 3-2, when constructing the market clearing model for the power trading center, the strategy set formed by the actual bids of distributed generation units is used. And the distributed energy system correlation and constraint model established through steps 1-3; Using a supply and demand curve construction method, the pricing of distributed generation units is... Electricity generation Mapped to the market supply-side curve Demand curves are generated by combining load-side electricity demand data. The reference formula for constructing the supply curve is: ; in: , These are the supply curve and the demand curve, respectively. It is the first i Quotation for a distributed generation unit, This represents the power generation of the distributed generation unit. For indicator functions; Based on market equilibrium conditions Combining the constraint of maximizing the revenue of distributed energy systems, an iterative optimization algorithm is used to solve for the clearing price of electricity. Simultaneously, the clearing price obtained from the solution will be... Quotation for distributed generation units In comparison, determine the actual power generation of each distributed generation unit: ; and , , These are the minimum and maximum bids, respectively. Introducing a balance verification mechanism to calculate the revenue deviation of distributed generation units: Overall market cost-benefit deviation:

[0017] If the deviation exceeds the threshold , If the deviation is not satisfactory, the system returns to adjust the supply and demand curve parameters or strategy input data and iterates again; if the deviation meets the requirements, the market clearing result is output. This completes the solution process for the clearing model; In sub-step 3-3, for each participant in the non-cooperative game (such as distributed generation units in the electricity market, and distributed generation units include distributed energy systems), taking the distributed energy system as an example, the second-order partial derivative of its payoff function is obtained: ; in, This represents the probability density coefficient of the net present value of revenue from a distributed energy system, reflecting the strength of the first-order influence of revenue on strategy variables. The second probability density coefficient of the net present value of revenue from a distributed energy system reflects the strength of the second-order influence of revenue on strategy variables.

[0018] In the solution space of this invention, since and It is obvious that: , Furthermore, in the scenario described in this invention, the benefits of the distributed energy system... Greater than cost Therefore, it can be deduced that , ,Right now Therefore, it can be concluded that no distributed generation unit can increase its revenue by unilaterally adjusting its own bidding price or power generation strategy, which verifies the existence of Nash equilibrium. Under the solution space of this invention and the above constraints, when the initial bid of the distributed generation unit is within a reasonable range, the iterative optimization algorithm will converge to a unique final strategy combination, which indicates that there is a unique Nash equilibrium point in the game model. The strategy combination corresponding to the Nash equilibrium state verified in the Nash equilibrium existence proof step is determined as the optimal strategy for all distributed generation units under the current market conditions, and it is output as the optimal decision scheme.

[0019] Compared with the prior art, the present invention has the following technical effects: 1) This invention introduces a market-based bidding and power clearing mechanism, breaking through the limitations of fixed on-grid electricity prices, achieving an increase in electricity price levels and optimized revenue, thereby increasing the total revenue of clean energy bases and small gas turbine bases by approximately 48% and 86% respectively, significantly enhancing economic efficiency.

[0020] 2) This invention integrates photovoltaic, wind power, and larger-scale energy storage and hydrogen storage systems in the planning process, effectively reducing the amount of abandoned electricity and increasing the overall clean energy consumption rate from 80.7% to 83.1%, thereby improving the utilization level of new energy.

[0021] 3) This invention constructs a competitive game and market clearing model for distributed generation units to ensure that all stakeholders can make optimal decisions, thereby improving the clean energy carrying capacity and operational reliability of the distribution network. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 The flowchart of the method for optimizing the carrying capacity of distributed clean energy in distribution networks based on market mechanisms is shown in the present invention. Figure 2 This is a schematic diagram illustrating the interaction between distributed clean energy and the power purchasing side in the power distribution network according to the present invention. Figure 3 This is a comparison chart of seasonal power generation at various energy bases in Method 1 and Method 2 of this invention. Detailed Implementation

[0023] A market-based optimization method for distributed clean energy carrying capacity in power distribution networks includes the following steps: Step 1: Construct a planning and decision-making model for the distributed energy system (hereinafter referred to as distributed energy system) and small gas turbines in the power distribution network; Step 2: Based on the market-oriented operation mode of the power purchase side distribution network realizing external power purchase through the upper-level power grid, and combined with the seasonal fluctuation characteristics of distributed new energy output, construct a power purchase decision optimization model for the power purchase side distribution network to obtain the decision scheme with the minimum total power purchase cost; Step 3: Based on the bidding strategy game model of distributed generation units (including distributed energy systems and small gas turbines), construct a market clearing model for the power trading center and derive the optimal decision scheme under this model.

[0024] Step 1 includes the following sub-steps: Sub-step 1-1: For distributed energy systems, with the goal of maximizing the total revenue of the planned project, conduct collaborative planning for the capacity of distributed energy sources such as photovoltaics and wind power, as well as multi-timescale energy storage, and then calculate the objective function; Sub-steps 1-2: Using the Black-Scholes option pricing model, combined with the unique characteristics of energy sector investment, to evaluate the value of uncertain options in the investment process of distributed energy system projects; Sub-steps 1-3: To ensure the economy and reliability of distributed energy system planning, and to meet the load demand of the power purchase side distribution network, a series of constraints related to distributed energy system planning are established. Sub-steps 1-4: Focusing on small gas turbines, with the goal of maximizing the total return on investment projects, conduct capacity planning and construct the corresponding objective function.

[0025] In sub-step 1-1, when establishing the distributed energy system as the objective function, the following sub-steps are further adopted: Step 1-1-1: First, introduce planning decision variables. Planning decision variables include unit capacity and investment scale; operational strategy variables are also introduced. Operational strategy variables include power generation dispatch and unit start-up and shutdown; Step 1-1-2: Introduce the first Typical season of the year Electricity price parameters ,when When distributed energy systems participate in electricity market price negotiations, the clearing price is used; when uncertainty is involved, the option value is the fixed feed-in tariff, and the clearing price is used. It is obtained through the electricity market price game mechanism and serves as a key market parameter for revenue calculation; Step 1-1-3: Construct an objective model with the maximization of total revenue of the distributed energy system as its core: (1); in, Let the electricity sales revenue function be the relationship between the electricity sales revenue function and the decision variables. , and clearing electricity prices Correlation, used to reflect market returns; The total cost is related to the planning variables. Related, covering investment costs Operation and maintenance costs fuel costs ; This refers to the maximum total revenue function of a distributed energy system; This refers to net present value; This refers to the value of options for investing in uncertainties; Step 1-1-4: Construct functions for the distributed energy system's power generation, electricity sales revenue, investment cost, operation and maintenance cost, and fuel cost respectively: (2); (3); (4); (5); (6); Given the need to improve the reliability of power supply in distributed energy systems, small gas turbines are typically configured. Therefore, the fuel cost of distributed energy systems mainly comes from these small gas turbines, and the fuel cost calculation formula is as follows: (7); Where N is the planning period; S is the typical season set; Let be the probability corresponding to scenario s; The number of days in a typical season; Electricity generated by distributed energy systems; r The discount rate is N; the planning period is N years. Powering small gas turbines; Contribute to photovoltaic power; Powering wind power; This refers to the energy storage discharge power; Powering fuel cells; Let x be the output power of device x at time t; For fuel prices; , For energy storage and hydrogen storage capacity; , , These are the operation and maintenance costs per unit capacity of equipment, the operation and maintenance costs per unit capacity of energy storage systems, and the operation and maintenance costs per unit capacity of hydrogen storage systems, respectively. This is the capital recovery factor; Let x be the investment cost per unit capacity of the equipment; a, b, and c are the fuel consumption coefficients corresponding to the small gas turbine.

[0026] In sub-steps 1-2, the obtained model is: (8); in, It is the standard normal probability distribution function; The risk-free rate of return on equipment investment; The volatility of asset value for equipment investment. , For parameters related to project benefits and costs, , This is an intermediate variable used to calculate the option value.

[0027] In sub-steps 1-3, when establishing the correlation relationships and supplementing the constraints of the distributed energy system model, the specific steps include: 1) The power balance constraint condition of the transmission system is: (9); in, Active power output for various types of power supplies; For the active power output of a small gas turbine; for t The system always has a constant active power load requirement; Power consumption for small gas turbines and other power sources; This refers to the active power output of the photovoltaic power source. This refers to the active power output of the wind power source. 2) The power transmission constraints of the distributed energy system are: (10); in, , Minimum and maximum transmission power; 3) Investment constraints are as follows: (11); in, , Minimum and maximum allowable capacity; 4) The operating constraints for photovoltaic and wind power output are as follows: (12); (13); in, , For active power output from photovoltaic and wind power; , The minimum allowable active power for photovoltaic power and the maximum allowable active power for wind power are specified. 5) The operating constraints for small gas turbines are: (14); in, , The minimum and maximum output power of a small gas turbine; For small gas turbines, the gradeability is... 6) The operating constraints of the hydrogen storage system are: (15); (16); (17); (18); (19); (20); in: This is the lower heating value of hydrogen. The charging efficiency of the electrolyzer represents the efficiency with which the electrolyzer converts electrical energy into hydrogen. , To improve the efficiency of charging and discharging hydrogen storage tanks; The discharge efficiency of a fuel cell represents the efficiency with which the fuel cell converts hydrogen into electrical energy. fors season t The hydrogen capacity in the hydrogen storage tank at any given time; for s season t The charging power of the electrolytic cell at all times; for s season t The discharge power of the fuel cell at any given time; , They are respectively s and s -1 Hydrogen capacity in the hydrogen storage tank at the start of the season; for s -1 Hydrogen capacity in the storage tank at the end of the season; For the season s The number of days; This is the maximum capacity of the hydrogen storage tank; , These are the maximum electrolysis power of the electrolyzer and the output power of the fuel cell, respectively. and A binary variable representing the operating state of the electrolyzer and the fuel cell; , These represent the hydrogen capacity in the hydrogen storage tanks at the beginning of the first season and the end of the last season, respectively. 7) The constraint model for the battery energy storage system is: (twenty one); (twenty two); (twenty three); (twenty four); in: , They are respectively t and t The amount of energy stored in the battery at time -1; , They are respectively t The charging or discharging power at any given time; , These represent the charge and discharge efficiencies of battery energy storage; This represents the maximum energy storage capacity of the battery. , These are binary variables representing the charging and discharging operating states, respectively. and These represent the battery's charge level at the beginning and end of the day, respectively. In sub-steps 1-4, the set of decision variables for the planning phase is introduced. J Decision variables in the planning stage With the set of decision variables during the operation phase Decision variables during the operation phase We construct a cost-relevant function for electricity sales revenue and simultaneously perform hierarchical optimization on the objective function of total revenue from small gas turbines, specifically including a single-layer optimization (an objective function model aimed at maximizing revenue): (25); Optimized returns from two-tier real options: (26); And construct a method for calculating carbon emission costs: (27); in, The actual carbon emissions per unit of electricity generated; Carbon quotas for small gas turbines; The price per unit of carbon emission rights; This is the benchmark value for power generation; This is the peak-shaving correction factor for the generating unit; In step 2, the specific sub-steps for constructing the power purchase decision optimization model for the power purchase side distribution network are as follows: Based on the specific power purchase plan, construct a system The matrix, with "minimizing the total load cost of the power purchase distribution network" as its core, serves as the guiding principle for decision-making. It calculates the power procurement cost and grid access fee separately, constructing corresponding mathematical expressions in stages: (28); (29); in, The cost of purchasing electricity for the power distribution network load on the purchasing side; The network access fees paid for the power distribution network load on the purchasing side; The fee for electricity transmitted over the network per unit of electricity; Construct a total cost model and correlate it with decision-making, establish the correlation between electricity consumption decisions and cost optimization, and output the electricity purchase combination that minimizes the total electricity purchase cost: (30); Step 3 includes the following sub-steps: Sub-step 3-1: Construct a game mechanism for the bidding strategy of distributed generation units to ensure that, in the power market environment, distributed generation units can formulate electricity price and electricity bidding strategies that maximize their own revenue, while the power purchase side distribution network can minimize the power purchase cost. Sub-step 3-2: Based on the bidding situation of distributed generation units, generation plans and the actual demand of the power grid, determine the final clearing price and power trading quota of the power market, and then construct the market clearing model of the power trading center; Sub-step 3-3: Prove that a Nash equilibrium exists in the non-cooperative game involving small gas turbines, and verify that the Nash equilibrium is unique and belongs to the optimal strategy.

[0028] In sub-step 3-1, a game model for the bidding strategy of distributed generation units is constructed. The game participants and strategy sets are defined, and the set of distributed generation units is denoted as . Each distributed generation unit Participation in the game; definition This represents the strategy combinations of each distributed generation unit participating in the game. In addition to distributed generation units k Pricing strategies for other distributed generation units, In addition to distributed generation units k Electricity reporting strategies for distributed generation units other than those mentioned above.

[0029] The criteria for determining the "optimal strategy" are: for any distributed generation unit... If a distributed generation unit's strategy Strategy combination for other distributed generation units All of these are optimal strategies, and this conclusion applies to any... If all conditions are met, the game reaches equilibrium, where all participants achieve optimal payoffs and costs. The specific mathematical expression is as follows: (31); in: , To achieve a clearing price and quantity in the game to reach equilibrium; , These are the game convergence thresholds for distributed generation units and the power purchase side distribution network, respectively.

[0030] In sub-step 3-2, when constructing the market clearing model for the power trading center, the strategy set formed by the actual bids of distributed generation units is used. And the distributed energy system correlation and constraint model established through steps 1-3.

[0031] Using a supply and demand curve construction method, the pricing of distributed generation units is... Electricity generation Mapped to the market supply-side curve Demand curves are generated by combining load-side electricity demand data. The reference formula for constructing the supply curve is: (32); in: , These are the supply curve and the demand curve, respectively. It is the first i Quotation for a distributed generation unit, This represents the power generation of the distributed generation unit. This is an indicator function.

[0032] Based on market equilibrium conditions Combining the constraint of maximizing the revenue of distributed energy systems, an iterative optimization algorithm is used to solve for the clearing price of electricity. Simultaneously, the clearing price obtained from the solution will be... Quotation for distributed generation units In comparison, determine the actual power generation of each distributed generation unit: (33); and , , These are the minimum and maximum bids, respectively. Introducing a balance verification mechanism to calculate the revenue deviation of distributed generation units: Overall market cost-benefit deviation:

[0033] If the deviation exceeds the threshold , If the deviation is not satisfactory, the system returns to adjust the supply and demand curve parameters or strategy input data and iterates again; if the deviation meets the requirements, the market clearing result is output. This completes the solution process for the clearing model; In sub-step 3-3, for each participant in the non-cooperative game (such as distributed generation units in the electricity market, and distributed generation units include distributed energy systems), taking the distributed energy system as an example, the second-order partial derivative of its payoff function is obtained as follows: (34); In the solution space of this invention, since and It is obvious that: , Furthermore, in the scenario described in this invention, the benefits of the distributed energy system... Greater than cost Therefore, it can be deduced that , ,Right now Therefore, it can be concluded that no distributed generation unit can increase its revenue by unilaterally adjusting its own bidding price or power generation strategy, which verifies the existence of Nash equilibrium. Under the solution space of this invention and the above constraints, when the initial bid of the distributed generation unit is within a reasonable range (where is the marginal cost and is the maximum electricity price on the purchasing side), the iterative optimization algorithm will converge to a unique final strategy combination, which indicates that there is a unique Nash equilibrium point in the game model. The strategy combination corresponding to the Nash equilibrium state verified in the Nash equilibrium existence proof step is determined as the optimal strategy for all distributed generation units under the current market conditions, and it is output as the optimal decision scheme.

[0034] Example: 1. Example Explanation: The following explanation uses a cross-regional system as an example, but this method is also applicable to scenarios involving the enhancement of distributed clean energy carrying capacity at the distribution network level. This chapter of the invention studies a cross-regional clean energy power system. The system structure can be mapped to a typical distribution network operating scenario with multiple distributed power sources. The power purchase side distribution network of this system focuses on load as the primary research object. The system only includes clean energy bases (such as green industrial parks) and small gas turbines, and does not involve thermal power equipment. The maximum annual load of the power purchase side distribution network is 65 MW. Currently, 8 MW of photovoltaic capacity and 12 MW of wind power capacity have been connected (both belonging to the clean energy base). Small gas turbines are not yet configured (to be planned). The planning period in the example is 20 years, and the simulation parameters involved in the model are shown in Table 1. The on-grid electricity prices for the clean energy base and small gas turbines in four typical seasons are shown in Appendix Table 2. It is assumed that the initial bidding strategy for both the clean energy base and the small gas turbines is a fixed on-grid electricity price; the volatility of the candidate asset value of the energy entity is 0.43, 0.55, and 0.66, respectively.

[0035] Table 1 Simulation Parameters

[0036] Table 2 Fixed On-Grid Electricity Price for Energy Bases

[0037] To verify the correctness and effectiveness of this method, simulations were performed on the examples of this invention using two different methods: Method 1: Baseline Method: Distributed Clean Energy Planning Method for Distribution Networks Using Fixed Feed-in Tariffs; Method 2: The method of this invention: a distributed clean energy planning method for distribution networks that considers market bidding and uncertain value.

[0038] 2. Simulation results: The planning schemes for clean energy bases using different methods are shown in Table 3, and the planning schemes for small gas turbines are shown in Table 4.

[0039] Table 3 Planning schemes for clean energy bases using different methods

[0040] Table 4. Small Gas Turbine Planning Schemes Using Different Methods

[0041] 3. Simulation results: To analyze the reasons for the significant changes in electricity sales revenue between clean energy bases and small gas turbine bases, Table 5 provides a comparison between the fixed on-grid electricity price for each energy base in Method 1 and the clearing price in Method 2.

[0042] Table 5. Comparison of fixed on-grid tariffs for various energy bases in Method 1 and clearing tariffs in Method 2 (RMB / MWh)

[0043] Regarding electricity prices, the clearing price of Method 2 (considering market bidding and real options) is higher than the fixed feed-in tariff for clean energy bases in Method 1 (fixed feed-in tariff), and the price difference exhibits significant seasonality. The clearing price is lowest and the difference is smallest in summer. The core reason is that from 10:00 to 16:00 in summer, distributed photovoltaic power generation in the power purchasing side's distribution network enters its peak output period, coupled with the simultaneous high output of photovoltaic power from clean energy bases, resulting in a significant increase in the total power supply in the region (clean energy base spring difference +43.0 yuan / MWh, summer +8.6 yuan / MWh, autumn +38.8 yuan / MWh, winter +55.8 yuan / MWh; small gas turbine base spring difference +30.7 yuan / MWh, summer +5.5 yuan / MWh, autumn +34.2 yuan / MWh, winter +40.4 yuan / MWh). At the same time, demand for electricity from outside the province decreases in summer, resulting in an overall market pattern of "supply exceeding demand." To increase electricity consumption and avoid power curtailment, clean energy bases moderately lower their bids to enhance market competitiveness, ultimately resulting in summer clearing prices being lower than those in other seasons, and the difference between these prices and the fixed price in Method 1 is minimal.

[0044] Regarding electricity generation, the power generation of each energy base in spring, summer, autumn and winter in Method 1 and Method 2 are as follows: Figure 3 As shown in the table, combined with the planning schemes and seasonal power generation data, it can be seen that the total power generation of the clean energy base in Method 2 is significantly higher than that in Method 1. This increase stems from Method 2 capturing high electricity price revenue through market bidding, while relying on expanded energy storage and hydrogen storage systems to smooth out power output fluctuations and improve absorption capacity, ultimately achieving increased power generation. The power generation of small gas turbines is basically the same in both methods. This difference reflects the planning logic of Method 2—under the guidance of market mechanisms, the proportion of clean energy power generation with more stable output and lower carbon emissions increases, and the power generation of small gas turbines is optimized and adjusted accordingly, which is in line with the core objective of the patent "improving the clean energy carrying capacity of the distribution network".

[0045] Simulation results show that the electricity price and power generation of clean energy bases under Method 2 are significantly higher than those under Method 1. The fundamental reason is that Method 1 uses a fixed feed-in tariff, and the energy bases do not reflect market competition, making it difficult to flexibly adjust according to supply and demand fluctuations. Method 2, on the other hand, introduces a distributed generation unit bidding strategy and an electricity market clearing mechanism, and incorporates real options methods to account for investment uncertainty, enabling clean energy bases to gradually increase electricity prices through bidding competition in a market-based environment until the market reaches equilibrium. Simultaneously, Method 2 coordinates and optimizes the capacity of photovoltaic, wind power, energy storage, and hydrogen storage at the planning level, enhancing the ability to adjust to fluctuations in new energy output. This not only improves electricity prices and sales revenue but also effectively increases the overall absorption rate of clean energy and the carrying capacity of the distribution network.

[0046] To verify the necessity of considering real options in the planning process of clean energy bases, the various costs of Method 1 and Method 2 are summarized in Table 5 below. Among them, the optimal value of the volatility of the candidate asset value is 0.6 in the planning process of Method 2.

[0047] Table 6. Cost-benefit comparison of various energy bases using Method 1 and Method 2 (Unit: RMB 10,000)

[0048] For clean energy bases, Method 2 includes larger-scale photovoltaic, wind power, and energy storage and hydrogen storage facilities in its planning, resulting in investment and operation and maintenance costs increasing by approximately 32.4% and 23.7% respectively compared to Method 1. Although the initial investment increased, the introduction of a market-based bidding mechanism significantly improved electricity prices and power generation absorption capacity, leading to an increase in electricity sales revenue from RMB 142.858 million to RMB 202.554 million, and a net present value (NPV) increase from RMB 92.278 million to RMB 136.62 million, resulting in an overall revenue increase of approximately 48.0%.

[0049] For small gas turbine bases, Method 2 also leads to increased investment and operation and maintenance costs. Furthermore, due to the expanded output scale, fuel costs and carbon emission costs also rise accordingly. However, the electricity price obtained through the market clearing mechanism in Method 2 is generally higher than that under the fixed-price model, driving up electricity sales revenue from RMB 47.368 million to RMB 67.232 million, and net present value from RMB 10.584 million to RMB 19.664 million, resulting in a total revenue increase of nearly 86%.

[0050] Specifically, Method 1 adopts a fixed feed-in tariff model, which lacks market competition among energy bases and makes it difficult to dynamically adjust prices based on supply and demand, thus limiting electricity prices and revenue levels. Method 2, on the other hand, introduces market-based bidding and electricity trading clearing mechanisms, enabling clean energy bases and small gas turbines to flexibly adjust their bids in the market, gain priority in electricity acceptance, and enhance output regulation capabilities by combining energy storage and hydrogen storage systems. This significantly increases electricity sales revenue and net present value, while increasing some investment and operating costs, achieving higher overall profitability and economic efficiency.

[0051] Table 7 Comparison of Renewable Energy Consumption in Clean Energy Bases

[0052] As shown in Table 7, Method 2 outperforms Method 1 in terms of clean energy utilization. Taking photovoltaic power as an example, the utilization rate of Method 1 is 78.8%, while Method 2 increases it to 81.3%; the utilization rate of wind power increases from 81.6% to 83.5%, and the overall utilization rate increases from 80.7% to 83.1%. This result indicates that Method 2 not only increases the actual power generation of clean energy but also effectively reduces the amount of power curtailed. The reason for this is that Method 1 uses a fixed feed-in tariff and lacks a market regulation mechanism, which easily leads to surplus power during peak periods of new energy output, resulting in a high amount of power curtailment. In contrast, Method 2 incorporates larger-scale energy storage and hydrogen storage systems in its planning, which can absorb surplus power during high output and release it during peak load periods, thereby improving power utilization; at the same time, the market-based bidding mechanism prioritizes clean energy entering the market, further improving the overall utilization rate and the clean energy carrying capacity of the distribution network.

[0053] In summary, the market-based optimization method for distributed clean energy carrying capacity in distribution networks proposed in this invention, compared to the traditional fixed feed-in tariff model, can improve electricity prices and generation absorption capacity by relying on market-based bidding and power clearing mechanisms, while increasing moderate investment and operation and maintenance costs. This results in a significant increase in the sales revenue and net present value of clean energy bases and small gas turbine bases, with total revenue increasing by approximately 48% and 86%, respectively. Simultaneously, the overall clean energy absorption rate increases from 80.7% to 83.1%, effectively enhancing the clean energy carrying capacity and operational economy of the distribution network. This invention comprehensively considers the coordinated planning of distributed energy systems and small gas turbines, market-based strategies on the power purchase side, and the bidding and clearing mechanism for generation units. It overcomes the shortcomings of existing distribution network planning, which struggles to simultaneously consider clean energy carrying capacity, investment uncertainty, and cost optimization. This achieves low-cost power purchase and efficient clean energy absorption, improves system reliability and flexibility, and effectively enhances the carrying capacity and operational economy of the distribution network.

Claims

1. A market-based optimization method for distributed clean energy carrying capacity in power distribution networks, characterized in that, Includes the following steps: Step 1: Construct a planning and decision-making model for the distributed energy system of the power distribution network, namely the distributed energy system and the small gas turbine; Step 2: Based on the market-oriented operation mode of the power purchase side distribution network realizing external power purchase through the upper-level power grid, and combined with the seasonal fluctuation characteristics of distributed new energy output, construct a power purchase decision optimization model for the power purchase side distribution network to obtain the decision scheme with the minimum total power purchase cost; Step 3: Based on the bidding strategy game model of distributed generation units, construct a market clearing model for the power trading center and derive the optimal decision scheme under this model; In step 3, the distributed generation unit includes a distributed energy system and a small gas turbine.

2. The method according to claim 1, characterized in that, Step 1 includes the following sub-steps: Sub-step 1-1: For distributed energy systems, with the optimization objective of maximizing the total revenue of the planned project, conduct collaborative planning for the capacity of distributed energy sources such as photovoltaics and wind power, as well as multi-timescale energy storage, and then calculate the objective function; Sub-steps 1-2: Using the Black-Scholes option pricing model, combined with the unique characteristics of energy sector investment, to evaluate the value of uncertain options in the investment process of distributed energy system projects; Sub-steps 1-3: To ensure the economy and reliability of distributed energy system planning, and to meet the load demand of the power purchase side distribution network, a series of constraints related to distributed energy system planning are established. Sub-steps 1-4: Focusing on small gas turbines, with the goal of maximizing the total return on investment projects, conduct capacity planning and construct the corresponding objective function.

3. The method according to claim 2, characterized in that, In sub-step 1-1, decision variables including unit capacity and investment scale are introduced during the planning phase, along with operational strategy variables including power generation dispatch schemes and unit start-up and shutdown status. Combined with electricity price parameters for typical seasons, a target model is constructed with the goal of maximizing the total revenue of the distributed energy system. ； in, Let the electricity sales revenue function be the relationship between the electricity sales revenue function and the decision variables. , and clearing electricity prices Correlation, used to reflect market returns; The total cost is related to the planning variables. Related, covering investment costs Operation and maintenance costs fuel costs ; This refers to the maximum total revenue function of a distributed energy system; This refers to net present value; This refers to the value of options for investing in uncertainties; Given the need to improve the reliability of power supply in distributed energy systems, small gas turbines are typically configured. Therefore, the fuel cost of distributed energy systems mainly comes from these small gas turbines, and the fuel cost calculation formula is as follows: ； Where N is the planning period; S is the typical season set; The number of days in a typical season; r The discount rate is N; the planning period is N years. denoted as fuel price; a, b, and c are the fuel consumption coefficients corresponding to small gas turbines.

4. The method according to claim 2, characterized in that, In sub-steps 1-2, the obtained model is: ； in, It is the standard normal probability distribution function; The risk-free rate of return on equipment investment; The volatility of asset value for equipment investment. , For parameters related to project benefits and costs, , This is an intermediate variable used to calculate the option value.

5. The method according to claim 2, characterized in that, In sub-steps 1-3, when establishing the correlation relationships and supplementing the constraints of the distributed energy system model, the specific steps include: 1) The power balance constraint condition of the transmission system is: ； in, Active power output for various types of power supplies; For the active power output of a small gas turbine; for t The system always has a constant active power load requirement; Power consumption for small gas turbines and other power sources; This refers to the active power output of the photovoltaic power source. This refers to the active power output of the wind power source. This refers to the number of small gas turbines installed. The number of power-consuming devices such as small gas turbines; This refers to the number of photovoltaic power supply units connected to the grid. This represents the number of wind power units connected to the grid. 2) The power transmission constraints of the distributed energy system are: ； in, , Minimum and maximum transmission power; 3) Investment constraints are as follows: ； in, , Minimum and maximum allowable capacity; 4) The operating constraints for photovoltaic and wind power output are as follows: ； ； in, , For active power output from photovoltaic and wind power; , The minimum allowable active power for photovoltaic power and the maximum allowable active power for wind power are specified. 5) The operating constraints for small gas turbines are: ； in, , The minimum and maximum output power of a small gas turbine; The gradeability of a small gas turbine; 6) The operating constraints of the hydrogen storage system are: ； ； ； ； ； ； in: This is the lower heating value of hydrogen. The charging efficiency of the electrolyzer represents the efficiency with which the electrolyzer converts electrical energy into hydrogen. , To improve the efficiency of charging and discharging hydrogen storage tanks; The discharge efficiency of a fuel cell represents the efficiency with which the fuel cell converts hydrogen into electrical energy. for s season t The hydrogen capacity in the hydrogen storage tank at any given time; for s season t The charging power of the electrolytic cell at all times; for s season t The discharge power of the fuel cell at any given time; , They are respectively s and s -1 Hydrogen capacity in the hydrogen storage tank at the start of the season; for s -1 Hydrogen capacity in the storage tank at the end of the season; For the season s The number of days; This is the maximum capacity of the hydrogen storage tank; , These are the maximum electrolysis power of the electrolyzer and the output power of the fuel cell, respectively. and A binary variable representing the operating state of the electrolyzer and the fuel cell; , These represent the hydrogen capacity in the hydrogen storage tanks at the beginning of the first season and the end of the last season, respectively. 7) The constraint model for the battery energy storage system is: ； ； ； ； in: , They are respectively t and t The amount of energy stored in the battery at time -1; , They are respectively t The charging or discharging power at any given time; , These represent the charge and discharge efficiencies of battery energy storage; This represents the maximum energy storage capacity of the battery. , These are binary variables representing the charging and discharging operating states, respectively. and These represent the battery's charge levels at the beginning and end of the day, respectively.

6. The method according to claim 2, characterized in that, In sub-steps 1-4, the set of decision variables for the planning phase is introduced. J Decision variables in the planning stage With the set of decision variables during the operation phase Decision variables during the operation phase We construct a function relating electricity sales revenue and costs, and simultaneously perform hierarchical optimization on the objective function of total revenue from small gas turbines. Specifically, this includes a first-level optimization to achieve the objective function model with the goal of maximizing revenue. ； Optimized returns from second-tier real options: ； And construct a method for calculating carbon emission costs: ； in, The actual carbon emissions per unit of electricity generated; Carbon quotas for small gas turbines; The price per unit of carbon emission rights; This is the benchmark value for power generation; This is the unit's peak-shaving correction factor.

7. The method according to claim 1, characterized in that, In step 2, the specific sub-steps for constructing the power purchase decision optimization model for the power purchase side distribution network are as follows: Based on the specific power purchase plan, construct a system The matrix is ​​centered around the principle of "minimizing the total load cost of the power distribution network on the power purchase side" as the guiding principle for decision-making. The power procurement cost and grid access fee are calculated separately, and corresponding mathematical expressions are constructed in stages. ； ； in, The cost of purchasing electricity for the power distribution network load on the purchasing side; The network access fees paid for the power distribution network load on the purchasing side; The fee for electricity transmitted over the network per unit of electricity; Construct a total cost model and correlate it with decision-making, establish the correlation between electricity consumption decisions and cost optimization, and output the electricity purchase combination that minimizes the total electricity purchase cost: 。 8. The method according to claim 1, characterized in that, Step 3 includes the following sub-steps: Sub-step 3-1: Construct a game mechanism for the bidding strategy of distributed generation units to ensure that, in the power market environment, distributed generation units can formulate electricity price and electricity bidding strategies that maximize their own revenue, while the power purchase side distribution network can minimize the power purchase cost. Sub-step 3-2: Based on the bidding situation of distributed generation units, generation plans and the actual demand of the power grid, determine the final clearing price and power trading quota of the power market, and then construct the market clearing model of the power trading center; Sub-step 3-3: Prove that a Nash equilibrium exists in the non-cooperative game involving small gas turbines, and verify that the Nash equilibrium is unique and belongs to the optimal strategy.

9. The method according to claim 8, characterized in that, In sub-step 3-1, a game model for the bidding strategy of distributed generation units is constructed; the game participants and strategy sets are defined, and the set of distributed generation units is denoted as . Each distributed generation unit Participation in the game; definition This represents the strategy combinations of each distributed generation unit participating in the game. In addition to distributed generation units k Pricing strategies for other distributed generation units, In addition to distributed generation units k Electricity reporting strategies for distributed generation units other than those mentioned above; The criteria for determining the "optimal strategy" are: for any distributed generation unit... If a distributed generation unit's strategy Strategy combination for other distributed generation units All of these are optimal strategies, and this conclusion applies to any... If all conditions are met, the game reaches equilibrium, where the payoffs and costs for all participants are optimal. The specific mathematical expression is as follows: ； in: , To achieve a clearing price and quantity in the game to reach equilibrium; , These are the game convergence thresholds for distributed generation units and the power purchase side distribution network, respectively.

10. The method according to claim 8, characterized in that, In sub-step 3-2, when constructing the market clearing model for the power trading center, the strategy set formed by the actual bids of distributed generation units is used. And the distributed energy system correlation and constraint model established through steps 1-3; Using a supply and demand curve construction method, the pricing of distributed generation units is... Electricity generation Mapped to the market supply-side curve Demand curves are generated by combining load-side electricity demand data. The reference formula for constructing the supply curve is: ； in: , These are the supply curve and the demand curve, respectively. It is the first i Quotation for each distributed generation unit, This represents the power generation of the distributed generation unit. For indicator functions; Based on market equilibrium conditions Combining the constraint of maximizing the revenue of distributed energy systems, an iterative optimization algorithm is used to solve for the clearing price of electricity. Simultaneously, the clearing price obtained from the solution will be... Quotation for distributed generation units In comparison, determine the actual power generation of each distributed generation unit: ； and , , These are the minimum and maximum bids, respectively. Introducing a balance verification mechanism to calculate the revenue deviation of distributed generation units: Overall market cost-benefit deviation: If the deviation exceeds the threshold , If the deviation is not satisfactory, the system returns to adjust the supply and demand curve parameters or strategy input data and iterates again; if the deviation meets the requirements, the market clearing result is output. This completes the solution process for the clearing model; In sub-step 3-3, for each participant in the non-cooperative game (such as distributed generation units in the electricity market, and distributed generation units include distributed energy systems), taking the distributed energy system as an example, the second-order partial derivative of its payoff function is obtained: ； in, This represents the probability density coefficient of the net present value of revenue from a distributed energy system, reflecting the strength of the first-order influence of revenue on strategy variables. The second probability density coefficient of the net present value of revenue from distributed energy systems reflects the strength of the second-order influence of revenue on strategy variables. In the solution space, because and It is obvious that: , Furthermore, in this scenario, the benefits of distributed energy systems... Greater than cost Therefore, it can be deduced that , ,Right now Therefore, it can be concluded that no distributed generation unit can increase its revenue by unilaterally adjusting its own bidding price or power generation strategy, which verifies the existence of Nash equilibrium. Under the solution space and the above constraints, when the initial bid of the distributed generation unit is within a reasonable range, the iterative optimization algorithm will converge to a unique final strategy combination, which indicates that there is a unique Nash equilibrium point in the game model. The strategy combination corresponding to the Nash equilibrium state verified in the Nash equilibrium existence proof step is determined as the optimal strategy for all distributed generation units under the current market conditions, and it is output as the optimal decision scheme.

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