A method and system for determining the influence of flexible resource response on quantity and price of a power spot market

Abstract

A method and system for determining the influence of flexible resource response on the quantity and price of a power spot market. The method includes quantitatively analyzing the adjustable capacity of a flexible resource based on the device response characteristics of the flexible resource, constructing a multi-time-scale dynamic response model of the flexible resource, and evaluating the adjustable performance of the flexible resource, and introducing an available coefficient to aggregate the flexible resources; based on the aggregation result of the flexible resources and combined with the trading rules of the power spot market, a market game model considering the participation of the flexible resources is constructed, and the model is solved by using the backward induction method; a coupling model of the power spot market and the response of the flexible resources is established by combining the dynamic response model of the flexible resources and the market game model, and the quantitative relationship between the response of the flexible resources and the electricity price of the power spot market is obtained. The scheme of the present application enables the flexible resource to quickly respond to the change of the electricity price and quickly feedback the state of the flexible resource, and realizes the dynamic quantity and price of the power spot market.

Description

Technical Field

[0001] This invention belongs to the field of electricity market and integrated energy system, and specifically relates to a method and system for determining the quantity and price impact of flexible resource response on the electricity spot market. Background Technology

[0002] As the power system transitions towards a higher proportion of renewable energy and marketization, the uncertainty of power source and load increases, the accuracy of predicting changes in power supply and demand decreases, and the required dispatch capacity of the power grid rises. The role of flexible resources (such as energy storage, combined heat and power, and distributed generation) in the electricity spot market is becoming increasingly prominent. However, traditional electricity market analysis often employs centralized optimization models, neglecting the strategic interaction characteristics between market participants. Single-entity decision-making cannot accurately depict the dynamic feedback mechanism of price and response during market game theory. Regarding the quantity and price in the electricity spot market, there is a lack of systematic methods to quantify the impact of flexible resource response characteristics on price fluctuations, peak-valley differences, and system costs. Existing evaluation indicators are mostly based on static scenarios, failing to quantify price fluctuations in the electricity spot market under flexible resource responses and failing to reflect the quantity-price interaction patterns in the dynamic game theory process. Traditional master-slave game model solution methods suffer from slow convergence iteration speeds and a tendency to get trapped in local optima. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for determining the quantitative and price impacts of flexible resource response on the electricity spot market, thereby solving the technical problem of the difficulty in quantifying the coupled influence between the flexible resource response of an integrated energy system and the electricity spot market price.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution.

[0005] This invention first discloses a method for determining the quantity and price impact of flexible resource response on the electricity spot market, the method comprising the following steps:

[0006] Step 1: Quantitatively analyze the adjustability of flexible resources based on the equipment response characteristics of flexible resources, construct a dynamic response model of flexible resources with multiple time scales to evaluate the adjustability performance of flexible resources, and introduce availability coefficients to aggregate flexible resources.

[0007] Step 2: Based on the results of flexible resource aggregation and combined with the rules of electricity spot market trading, construct a market game model that considers the participation of flexible resources, and solve the model using the backward induction method;

[0008] Step 3: Combine the flexible resource dynamic response model with the market game model to establish a coupling model between the electricity spot market and the flexible resource response, and obtain the quantitative relationship between the flexible resource response and the electricity spot market price.

[0009] The present invention further includes the following preferred embodiments:

[0010] The quantitative analysis of the adjustability of flexible resources based on the equipment response characteristics further includes:

[0011] The adjustability of flexible resources is quantitatively analyzed based on the response time of flexible resource devices and the adjustable power range of flexible resources at different time scale levels.

[0012] Regarding the response time of flexible resource devices, based on the response speed and self-adjustment capability of the flexible resource devices, flexible resources are divided into: second-level, minute-level, and hour-level; the device response time is:

[0013]

[0014] In the formula, , , The first i The response time, total configuration capacity, and adjustable power limit per unit time of flexible resource-like equipment;

[0015] The adjustable power range of the flexible resources is:

[0016]

[0017] In the formula, For flexible resource equipment i Adjustable power range; , These are the upper limit coefficient and lower limit coefficient of output for flexible resource equipment, respectively.

[0018] The construction of a multi-timescale flexible resource dynamic response model further includes:

[0019] For second-level response, calculate the real-time charge and discharge power of electrochemical energy storage. :

[0020]

[0021] In the formula, , These are the energy storage charging power and the discharging power, respectively. , , These are real-time electricity price, off-peak electricity price, and peak electricity price, respectively.

[0022] For minute-level response, calculate the real-time power adjustment of the air conditioner. :

[0023]

[0024] In the formula The temperature control elasticity coefficient; This is the base power for the air conditioning load; The market benchmark electricity price;

[0025] Calculate the real-time output power of an air source heat pump :

[0026]

[0027] In the formula, This is the maximum output power of the heat pump; This is the maximum output power of the CCHP (Combined Gas Harmonized System). The threshold for electricity price for full-load operation of the heat pump; The electricity price sensitivity coefficient for air source heat pumps;

[0028] For responses lasting hours or longer:

[0029] Calculate the real-time output power of natural gas combined cooling, heating, and power (CCHP). :

[0030]

[0031] In the formula, This is the minimum output power for a combined cooling, heating, and power system for natural gas.

[0032] Calculate the real-time output power of the cold / heat storage device :

[0033]

[0034] In the formula, , These refer to the energy storage capacity and energy release capacity of the cold / heat storage device, respectively.

[0035] Calculate the power consumption of renewable energy output :

[0036]

[0037] In the formula, Forecasted output for renewable energy equipment; The price elasticity coefficient for renewable energy; This refers to the amount of renewable energy that is abandoned due to grid absorption capacity limitations.

[0038] The assessment of the adjustability of flexible resources further includes:

[0039] Construct an equivalent model for the aggregated cluster:

[0040]

[0041] In the formula, as a flexible resource aggregator t The power can always be adjusted up or down; For the first i The availability coefficient of flexible resources; For the first i Flexible resources t Adjustable power up / down at any time ; This refers to the aggregated loss of flexible resources; N This represents the total number of flexible resource equipment types.

[0042] For assessing the regulation potential, the response speed is calculated. :

[0043]

[0044] In the formula, For time intervals; For the first i Flexible resource equipment in t Power output at any given moment; For the first i Flexible resource equipment in Power output at any given moment;

[0045] The adjustment potential index was normalized:

[0046]

[0047] In the formula, To adjust the potential normalization value, ; , These represent the maximum and minimum values ​​of the flexible resource adjustment potential, respectively.

[0048] Calculate adjustment accuracy :

[0049]

[0050] In the formula, X To adjust the number of sampling points within the adjustment period; For the first i Flexible resource devices at all times t Target regulating power;

[0051] The adjustment accuracy index is normalized:

[0052]

[0053] In the formula, To adjust the accuracy normalization value, ; , These represent the maximum and minimum values ​​for flexible resource adjustment accuracy, respectively.

[0054] Calculate the flexibility index :

[0055]

[0056] In the formula, K For time scale levels; For the first k Weights for each time scale; , The first i The upward and downward adjustable power of flexible resource-type equipment; For the first i Rated power of flexible resource equipment;

[0057] The flexibility index is normalized:

[0058]

[0059] In the formula, This is a normalized value for the flexibility index. ; , These represent the maximum and minimum values ​​of the flexibility index for flexible resources, respectively.

[0060] Based on the aforementioned adjustable performance indicators of flexible resources, the dynamic availability coefficient of flexible resources is quantified. Under different scenarios, the availability coefficient of flexible resources changes dynamically according to the scenario. The quantification method is as follows:

[0061]

[0062] In the formula, This refers to the dynamic availability coefficient of flexible resources. , , They are respectively t Weighting coefficients for adjustment potential, adjustment accuracy, and flexibility indicators in real-time scenarios.

[0063] The construction of the market game model that considers the participation of flexible resources further includes:

[0064] Constructing a day-ahead clearing electricity price model with flexible resource participation based on electricity spot market trading rules:

[0065]

[0066] In the formula, The electricity price was cleared out a few days ago; For conventional units j The reported electricity volume of the day before; For flexible resource equipment i The reported electricity volume of the day before; For conventional units j The electricity price declared before the date; For flexible resource equipment i The electricity price declared before the date; M This refers to the total number of conventional unit types submitted for approval recently.

[0067] Based on the day-ahead clearing electricity price model, a master-slave game equilibrium model with flexible resource aggregation participation is constructed, including an upper-level leader optimization model and a lower-level follower optimization model.

[0068] The objective function of the upper-level leader optimization model is:

[0069]

[0070] In the formula, To adjust the total cost of the system; T The duration of the scheduling cycle; To adjust costs for flexible resources; This is the penalty coefficient for electricity price fluctuations; For the reference electricity price in the spot market, either the historical average or the policy target price can be used. Real-time status of energy storage devices;

[0071] The objective function of the lower-level follower optimization model is:

[0072]

[0073] In the formula, For the total revenue of flexible resource adjustment; This refers to the operating costs of flexible resources.

[0074] The method of solving the model using backward induction further includes:

[0075] For the problem of solving a two-layer master-slave game model in which the upper-layer entity is a market operator with the goal of minimizing the total system cost, and the lower-layer entity is a flexible resource aggregator with the goal of maximizing adjustment revenue, a backward induction method based on multiple initial points and hot start is used to solve the game between the upper and lower layers and obtain the optimal equilibrium solution.

[0076] In the multi-initial-point sampling stage, the initial electricity price is selected based on a random sampling method, i.e., within the price range. Endogenous generation R Group initial electricity price curve;

[0077]

[0078] In the formula, To initialize the electricity price; It is a uniform sampling function; , These are the minimum allowable electricity price and the maximum allowable electricity price, respectively.

[0079] The warm start strategy is as follows:

[0080] In a certain iteration, the optimization functions of the upper and lower layers change gradually, and the current solution is retained as a candidate for a warm start; in parallel computing, the warm start solution is added to the initial point set, replacing threads with slower convergence; according to t Optimal solution for the time period, calculation t The optimal solution for the +1 time period is used as the initialization variable for hot start.

[0081]

[0082] In the formula, for t The optimal solution for the time period; for t +1 Optimal solution for time period; This is a correction term based on electricity price trends;

[0083] In parallel inverse inductive solution, multiple upper and lower-level optimizations based on the initial point are performed simultaneously:

[0084] An initial electricity price is given during initialization. Set the maximum number of iterations. Q Convergence threshold ;

[0085] Based on the given initial electricity price, the lower-level optimization model is solved to derive the lower-level response strategy. ;

[0086] Based on the cost function and inequality constraints, a Lagrangian function is constructed. Then, the derivative with respect to the aggregator adjustment is taken and set to zero to obtain the lower-level optimal solution.

[0087]

[0088] In the formula, e , s These are the unit adjustment cost and the marginal cost coefficient, respectively.

[0089] According to the lower-level response strategy The upper levels adjust electricity prices to minimize total regulation costs;

[0090] Updating electricity prices based on gradient descent:

[0091]

[0092] In the formula, , The first r The initial point is at the _ ... q The next iteration, the... q The electricity price for +1 iteration; The projection operator ensures that the electricity price remains within the limit. This is the gradient descent step size;

[0093] The convergence criterion is:

[0094]

[0095] When the above conditions are met, save the equilibrium solution at this time. Otherwise, continue iterating until the required number of iterations is reached.

[0096] Step 3 further includes:

[0097] The electricity spot market price after flexible resource response is calculated based on the equilibrium solution of the master-slave game model.

[0098] The flexible resource equipment in the integrated energy system adjusts its output according to the dispatch instructions issued by the dispatch center. The system dispatch instructions are generated based on the spot market electricity price signal. During peak electricity price periods, generator sets and energy storage equipment increase output to meet load demand and reduce electricity prices. During off-peak electricity price periods, generator sets reduce output, energy storage equipment increases charging power, and the load is shifted to this period. The electricity spot market price signal guides the flexible resource equipment in the integrated energy system to adjust its output. At the same time, after responding to the dispatch instructions, the flexible resource equipment changes its operating status, thereby affecting the spot market electricity price.

[0099] The final equilibrium solution obtained from the above master-slave game model The flexible resource aggregator issues adjustment commands to the flexible resource equipment. After responding to the commands, the flexible resources will change their output, thereby affecting the electricity supply and demand relationship and thus adjusting the electricity spot market price. The adjustment mechanism is as follows:

[0100]

[0101] In the formula, The revised spot market electricity price; for t System load at any given time; The slope of the marginal cost curve for power generation;

[0102] This study analyzes the coupling relationship between flexible resources and electricity prices, using the price elasticity coefficient to reflect the sensitivity of flexible resource responses to changes in electricity prices.

[0103]

[0104] In the formula, This represents the change in the aggregation power of flexible resources. This represents the change in spot market electricity prices;

[0105] Analyze the impact of flexible resource equipment's power adjustment in one time period on electricity prices in another time period, calculate its sensitivity coefficient, and construct a sensitivity matrix:

[0106]

[0107] In the formula, For the first i Flexible resources t Power regulation during time periods t a The impact of time-of-use electricity pricing; for t a Market electricity prices during certain time periods.

[0108] This invention also discloses a system for determining the quantity and price impact of flexible resource response on the electricity spot market using the aforementioned method for determining the quantity and price impact of flexible resource response on the electricity spot market, comprising:

[0109] The flexible resource adjustability analysis module is used to quantitatively analyze the adjustability of flexible resources based on the equipment response characteristics of flexible resources, construct a dynamic response model of flexible resources at multiple time scales, evaluate the adjustability performance of flexible resources, and introduce availability coefficients to aggregate flexible resources.

[0110] The market game model construction module is used to construct a market game model that considers the participation of flexible resources based on the aggregation results of flexible resources and combined with the trading rules of the electricity spot market, and to solve the model using the backward induction method.

[0111] The electricity spot market price impact module is used to combine the flexible resource dynamic response model with the market game model to establish a coupled model of the electricity spot market and flexible resource response, and obtain the quantitative relationship between flexible resource response and electricity spot market price.

[0112] Accordingly, this application also discloses a terminal, including a processor and a storage medium;

[0113] The storage medium is used to store instructions;

[0114] The processor is configured to operate according to the instructions to execute the steps of the method for determining the quantity and price impact of the aforementioned flexible resource response on the electricity spot market.

[0115] Accordingly, this application also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for determining the quantity and price impact of flexible resource response on the electricity spot market.

[0116] The beneficial effects of this invention are as follows: Compared with the prior art, this invention provides a method and system for determining the impact of flexible resource response on the quantity and price of the electricity spot market. Based on traditional static evaluation indicators, a multi-timescale dynamic response model for flexible resources is constructed, and a master-slave game equilibrium model considering the participation of flexible resources is built based on market trading rules. The model is solved using backward induction, and a closed-loop feedback mechanism of flexible resource response and electricity spot market price changes is proposed based on the dynamic equilibrium of the objectives of market operators and flexible resource aggregators. Building upon the traditional backward induction method, a method of parallel optimization with multiple initial points and hot-start adjustment of initial points is introduced, solving the problems of slow iterative convergence and susceptibility to local optima in the traditional backward induction method. This enables flexible resources to respond quickly to price changes and rapidly feedback their status, realizing dynamic quantity and price in the electricity spot market. By establishing a sensitivity coefficient matrix, the marginal impact of single flexible resource adjustments on the electricity spot market price can be analyzed, and the impact dimensions can be accurately quantified. Attached Figure Description

[0117] Figure 1 This is a flowchart of the method for determining the quantity and price impact of flexible resource response on the electricity spot market in this invention.

[0118] Figure 2 This is a flowchart of the master-slave game model solution based on the reverse induction method of introducing multiple initial points and hot start in this invention. Detailed Implementation

[0119] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0120] The embodiments described in this application are merely some, not all, embodiments of the present invention. Based on the spirit of the present invention, other embodiments obtained by those skilled in the art without inventive effort are all within the protection scope of the present invention.

[0121] To address the shortcomings of existing technologies in quantifying price fluctuations caused by flexible resource responses in the electricity spot market environment, and the inability of single-entity decision-making to reflect the coupled competition among multiple market participants, this invention proposes a method and system for determining the quantity and price impact of flexible resource responses on the electricity spot market. This method is applicable to the quantitative analysis of the impact of flexible resources such as combined heat and power (CHP), renewable energy, and electrochemical energy storage on electricity spot market prices and traded volumes. A master-slave game equilibrium model considering the participation of flexible resource responses is constructed, achieving closed-loop feedback regulation of "flexible resource response - electricity price change". Combining traditional backward induction and random sampling methods, multiple initial points and hot start are introduced to solve the master-slave game model, deriving the optimal equilibrium solution for electricity prices and flexible resource response strategies. This leads to the construction of a quantity and price analysis model for flexible resource responses in the electricity spot market, realizing the dynamic quantity and price of the electricity spot market under flexible resource responses, i.e., the impact of flexible resource responses on the quantity and price of the electricity spot market.

[0122] See Figure 1 As shown, the method for determining the quantity and price impact of flexible resource response on the electricity spot market disclosed in this invention includes the following steps:

[0123] Step 1: Quantitatively analyze the adjustability of flexible resources based on the equipment response characteristics of flexible resources, construct a dynamic response model of flexible resources with multiple time scales, evaluate the adjustability performance of flexible resources, and introduce availability coefficients to aggregate flexible resources.

[0124] In a specific embodiment, step 1 includes the following steps:

[0125] Step 101: Quantitatively analyze the adjustability of flexible resources based on the response time of flexible resource devices and the adjustable power range of flexible resources at different time scale levels.

[0126] For the response time of flexible resource equipment, based on the response speed and self-regulation capability of the equipment, flexible resources are divided into: second-level (e.g., electrochemical energy storage), minute-level (e.g., air source heat pumps and air conditioners), and hour-level (e.g., combined cooling, heating, and power systems, thermal and cold storage devices, and renewable energy). The equipment response time is calculated as follows:

[0127]

[0128] In the formula, , , The first i The response time (time required to go from maximum power to full response) of flexible resource devices, the total configured capacity, and the upper limit of adjustable power per unit time.

[0129] Due to inherent physical and operational constraints, the equipment cannot achieve maximum adjustable power output. Considering these constraints, the adjustable power range for flexible resources is as follows:

[0130]

[0131] In the formula, For flexible resource equipment i Adjustable power range; , These are the upper limit coefficient and lower limit coefficient of output for flexible resource equipment, respectively.

[0132] Step 102: Construct a multi-timescale flexible resource dynamic response model for responses at different time scale levels.

[0133] Step 1021: For second-level response, calculate the real-time charge and discharge power of electrochemical energy storage. :

[0134]

[0135] In the formula, , These are the energy storage charging power and the discharging power, respectively. , , These are real-time electricity price, off-peak electricity price, and peak electricity price, respectively.

[0136] Step 1022: For minute-level responses:

[0137] Calculate the real-time power adjustment of the air conditioner :

[0138]

[0139] In the formula The temperature control elasticity coefficient; This is the base power for the air conditioning load; It is the market benchmark electricity price.

[0140] Calculate the real-time output power of an air source heat pump :

[0141]

[0142] In the formula, This is the maximum output power of the heat pump; This is the maximum output power of the CCHP (Combined Gas Harmonized System). The threshold for electricity price for full-load operation of the heat pump; This is the electricity price sensitivity coefficient for air source heat pumps.

[0143] Step 1023: For responses of hour level and above:

[0144] Calculate the real-time output power of natural gas combined cooling, heating, and power (CCHP). :

[0145]

[0146] In the formula, This is the minimum output power for a combined cooling, heating, and power system for natural gas.

[0147] Calculate the real-time output power of the cold / heat storage device :

[0148]

[0149] In the formula, , These refer to the energy storage power and energy release power of the cold / heat storage device, respectively.

[0150] Calculate the power consumption of renewable energy output :

[0151]

[0152] In the formula, Forecasted output for renewable energy equipment; The price elasticity coefficient for renewable energy; This refers to the amount of renewable energy that is abandoned due to grid absorption capacity limitations.

[0153] Step 103: Select flexible resource adjustment capability indicators, quantify the adjustable performance of flexible resources, and introduce resource availability coefficients to construct a flexible resource aggregation model.

[0154] For assessing the regulation potential, the response speed is calculated. :

[0155]

[0156] In the formula, For time intervals; For the first i Flexible resource equipment in t Power output at any given moment; For the first i Flexible resource equipment in The output power at any given moment.

[0157] The adjustment potential index was normalized:

[0158]

[0159] In the formula, To adjust the potential normalization value, ; , These represent the maximum and minimum potential for flexible resource adjustment, respectively.

[0160] Calculate adjustment accuracy :

[0161]

[0162] In the formula, X To adjust the number of sampling points within the adjustment period; For the first i Flexible resource devices at all times t The target adjustment power.

[0163] The adjustment accuracy index is normalized:

[0164]

[0165] In the formula, To adjust the accuracy normalization value, ; , These represent the maximum and minimum values ​​for flexible resource adjustment accuracy, respectively.

[0166] Calculate the flexibility index :

[0167]

[0168] In the formula, K Time scale levels, such as seconds, minutes, hours, etc.; For the first k Weights for each time scale; , The first i The upward and downward adjustable power of flexible resource-type equipment; For the first i Rated power of flexible resource equipment.

[0169] The flexibility index is normalized:

[0170]

[0171] In the formula, This is a normalized value for the flexibility index. ; , These represent the maximum and minimum values ​​of the flexibility index for flexible resources.

[0172] Based on the aforementioned adjustable performance indicators of flexible resources, the dynamic availability coefficient of flexible resources is quantified. Under different scenarios, the availability coefficient of flexible resources changes dynamically according to the scenario. The quantification method is as follows:

[0173]

[0174] In the formula, This refers to the dynamic availability coefficient of flexible resources. , , They are respectively t Weighting coefficients for adjustment potential, adjustment accuracy, and flexibility indicators in real-time scenarios.

[0175] In the context of the electricity spot market, resource aggregators aggregate flexible resource adjustability to participate in the spot market and profit, constructing an equivalent model of aggregation clusters:

[0176]

[0177] In the formula, as a flexible resource aggregator t The power can always be adjusted up or down; For the first i The availability coefficient of flexible resources; For the first i Flexible resources t Adjustable power up / down at any time ; This refers to the aggregated loss of flexible resources; N This represents the total number of flexible resource equipment types.

[0178] Step 2: Based on the results of flexible resource aggregation and combined with the trading rules of the electricity spot market, construct a market game model that considers the participation of flexible resources, and solve the model using the backward induction method.

[0179] In a specific embodiment, step 2 includes the following steps:

[0180] Step 201: Construct a day-ahead clearing electricity price model with flexible resource participation based on the electricity spot market trading rules.

[0181] Flexible resource equipment participates in the electricity spot market primarily by submitting quantity and price bids during the day-ahead phase. Its output is determined by the day-ahead clearing plan curve, and adjustments are made based on intraday and real-time system conditions. The day-ahead clearing price model is as follows:

[0182]

[0183] In the formula, The electricity price was cleared out a few days ago; For conventional unitsj The reported electricity volume of the day before; For flexible resource equipment i The reported electricity volume of the day before; For conventional units j The electricity price declared before the date; For flexible resource equipment i The electricity price declared before the date; M This refers to the total number of conventional unit types submitted for approval recently.

[0184] Step 202: Construct a master-slave game equilibrium model based on the day-ahead clearing electricity price model, including an upper-level leader optimization model and a lower-level follower optimization model.

[0185] Flexible resource aggregators can effectively reduce system regulation reserve capacity and system dispatch costs by adjusting their output according to electricity spot market price signals. In the process of integrated energy system optimization and dispatch, market operators need to consider factors such as system physical constraints and market fairness, with the goal of minimizing the total system cost. Flexible resource aggregators, on the other hand, need to consider their own output constraints and regulation capacity boundaries, with the goal of maximizing their own profits.

[0186] The objective function of the upper-level leader optimization model is:

[0187]

[0188] In the formula, To adjust the total cost of the system; T The duration of the scheduling cycle; To adjust costs for flexible resources; This is the penalty coefficient for electricity price fluctuations; For the reference electricity price in the spot market, either the historical average or the policy target price can be used. This indicates the real-time status of the energy storage device.

[0189] The constraints include:

[0190] System power balance constraints:

[0191]

[0192] In the formula, G , F These are respectively the set of flexible resource equipment after the set of conventional generator sets; for t Total system load demand at any time; For conventional generator sets j exist t Efforts made at all times; for t Total network loss of the system at any time.

[0193] Conventional unit operating constraints:

[0194]

[0195]

[0196] In the formula, , They are conventional units j Minimum output power and maximum output power; For conventional units j exist t The output at +1 moment; For conventional units j Maximum climbing rate.

[0197] Flexible resource adjustment constraints:

[0198]

[0199] In the formula, , Flexible resource equipment i exist t The minimum and maximum adjustable power at any given time.

[0200] Cybersecurity constraints:

[0201]

[0202] In the formula, L A collection of transmission lines; for t Timetable l The meritorious trend; For the line l Maximum secure transmission capacity.

[0203] The objective function of the lower-level follower optimization model is:

[0204]

[0205] In the formula, For the total revenue of flexible resource adjustment; This refers to the operating costs of flexible resources.

[0206] Constraints:

[0207] Flexible resource physical constraints:

[0208]

[0209]

[0210]

[0211] In the formula, , , Flexible distributed power sources x The real-time power generation, minimum output power, and maximum output power, among which x Represents CCHP and distributed photovoltaics; , These are the minimum and maximum charging / discharging power of the energy storage device, respectively. , , Flexible adjustable load y The real-time power generation, minimum output power, and maximum output power, among which y This represents air conditioning load and air source heat pump.

[0212] Response capability constraints:

[0213]

[0214]

[0215] In the formula, For flexible resource aggregators t Total adjustable power at +1 time; The maximum rate of change of the total adjustable power of the flexible resource aggregator.

[0216] Market clearing constraints:

[0217]

[0218] In the formula, , These represent the minimum and maximum up / down adjustable power available to flexible resource aggregators.

[0219] Step 203: Solve the master-slave game model using backward induction based on multiple initial points and hot start.

[0220] See Figure 2 For the problem of solving a two-layer master-slave game model in which the upper-layer entity is a market operator with the goal of minimizing the total system cost, and the lower-layer entity is a flexible resource aggregator with the goal of maximizing adjustment revenue, a backward induction method based on multiple initial points and hot start is used to solve the game between the upper and lower layers and obtain the optimal equilibrium solution.

[0221] 1) Multiple initial point generation and warm start

[0222] In the multi-initial-point sampling stage, the initial electricity price is selected based on a random sampling method, i.e., within the price range. Endogenous generation R Group initial electricity price curve.

[0223]

[0224] In the formula, To initialize the electricity price; It is a uniform sampling function; , These are the minimum allowable electricity price and the maximum allowable electricity price, respectively.

[0225] The warm start strategy is as follows:

[0226] In a certain iteration, the optimization functions of the upper and lower layers change gradually, and the current solution is retained as a candidate for a warm start; in parallel computing, the warm start solution is added to the initial point set, replacing the thread with slower convergence. According to t Optimal solution for the time period, calculation t The optimal solution for the +1 time period is used as the initialization variable for warm start.

[0227]

[0228] In the formula, for t The optimal solution for the time period; for t +1 Optimal solution for time period; This is a correction term based on electricity price trends.

[0229] 2) Parallel reverse induction solution

[0230] Multiple optimizations based on the initial point are performed simultaneously at different levels, and the process is as follows.

[0231] An initial electricity price is given during initialization. Set the maximum number of iterations. Q Convergence threshold .

[0232] Based on the given initial electricity price, the lower-level optimization model is solved to derive the lower-level response strategy. .

[0233] Based on the cost function and inequality constraints, a Lagrangian function is constructed. Then, the derivative with respect to the aggregator adjustment is taken and set to zero to obtain the lower-level optimal solution.

[0234]

[0235] In the formula, e , s These are the unit adjustment cost and the marginal cost coefficient, respectively.

[0236] According to the lower-level response strategy The upper level adjusts electricity prices to minimize total regulation costs.

[0237] Updating electricity prices based on gradient descent:

[0238]

[0239] In the formula, , The first r The initial point is at the _ ... q The next iteration, the... q The electricity price for +1 iteration; The projection operator ensures that the electricity price remains within the limit. This is the gradient descent step size.

[0240] The convergence criterion is:

[0241]

[0242] When the above conditions are met, save the equilibrium solution at this time. Otherwise, continue iterating until the required number of iterations is reached.

[0243] Step 3: Combine the flexible resource dynamic response model with the market game model to establish a coupling model between the electricity spot market and the flexible resource response, and obtain the quantitative relationship between the flexible resource response and the electricity spot market price.

[0244] In a preferred embodiment, step 3 includes the following steps:

[0245] Step 301: Calculate the spot market electricity price after flexible resource response based on the equilibrium solution of the master-slave game model.

[0246] In a spot electricity market environment, flexible resource devices in an integrated energy system adjust their output according to dispatch instructions issued by the dispatch center. These dispatch instructions are generated based on spot market electricity price signals. During peak electricity price periods, generators and energy storage devices increase output to meet load demand and lower prices; during off-peak periods, generators reduce output, energy storage devices increase charging power, and adjustable loads such as heat pumps shift to operation during these periods. The spot electricity market price signal guides the flexible resource devices in the integrated energy system to adjust their output. Simultaneously, the flexible resource devices, in response to dispatch instructions, change their operating status, thereby influencing spot market electricity prices, forming a closed-loop optimization process of "electricity price signal guidance - flexible resource device response - spot market quantity and price changes."

[0247] The final equilibrium solution obtained from the above master-slave game model The flexible resource aggregator issues adjustment commands to the flexible resource equipment. After responding to the commands, the flexible resources will change their output, thereby affecting the electricity supply and demand relationship and thus adjusting the electricity spot market price. The adjustment mechanism is as follows:

[0248]

[0249] In the formula, The revised spot market electricity price; for t System load at any given time; This represents the slope of the marginal cost curve for power generation.

[0250] Step 302: Construct a coupled model of electricity spot market price and flexible resource response, and analyze the relationship between flexible resource response and electricity spot market price and quantity.

[0251] 1) Analyze the coupling relationship between flexible resources and electricity prices, and use the price elasticity coefficient to reflect the sensitivity of flexible resource response to changes in electricity prices.

[0252]

[0253] In the formula, This represents the change in the aggregation power of flexible resources. This represents the change in spot market electricity prices.

[0254] 2) Analyze the impact of the power adjustment of flexible resource equipment in a certain period on the electricity price in another period, calculate its sensitivity coefficient, and construct a sensitivity matrix.

[0255]

[0256] In the formula, For the first i Flexible resources t Power regulation during time periods t a The impact of time-of-use electricity pricing; for t a Market electricity prices during certain time periods.

[0257] The above analysis allows us to measure the overall supply and demand elasticity of the market and the marginal impact of specific flexible resources on electricity prices, thus achieving a closed-loop path of "electricity price signal - flexible resource response - electricity spot market quantity and price analysis".

[0258] The beneficial effects of this invention are as follows: Compared with the prior art, this invention provides a method and system for determining the impact of flexible resource response on the quantity and price of the electricity spot market. Based on traditional static evaluation indicators, a multi-timescale dynamic response model for flexible resources is constructed, and a master-slave game equilibrium model considering the participation of flexible resources is built based on market trading rules. The model is solved using backward induction, and a closed-loop feedback mechanism of flexible resource response and electricity spot market price changes is proposed based on the dynamic equilibrium of the objectives of market operators and flexible resource aggregators. Building upon the traditional backward induction method, a method of parallel optimization with multiple initial points and hot-start adjustment of initial points is introduced, solving the problems of slow iterative convergence and susceptibility to local optima in the traditional backward induction method. This enables flexible resources to respond quickly to price changes and rapidly feedback their status, realizing dynamic quantity and price in the electricity spot market. By establishing a sensitivity coefficient matrix, the marginal impact of single flexible resource adjustments on the electricity spot market price can be analyzed, and the impact dimensions can be accurately quantified.

[0259] This invention can be a system, method, and / or computer program product. This invention also discloses a system for determining the quantity and price impact of flexible resource response on the electricity spot market, based on the aforementioned method for determining the quantity and price impact of flexible resource response on the electricity spot market, comprising:

[0260] The flexible resource adjustability analysis module is used to quantitatively analyze the adjustability of flexible resources based on the equipment response characteristics of flexible resources, construct a dynamic response model of flexible resources at multiple time scales, evaluate the adjustability performance of flexible resources, and introduce availability coefficients to aggregate flexible resources.

[0261] The market game model construction module is used to construct a market game model that considers the participation of flexible resources based on the aggregation results of flexible resources and combined with the trading rules of the electricity spot market, and to solve the model using the backward induction method.

[0262] The electricity spot market price impact module is used to combine the flexible resource dynamic response model with the market game model to establish a coupled model of the electricity spot market and flexible resource response, and obtain the quantitative relationship between flexible resource response and electricity spot market price.

[0263] Based on the spirit of this invention, those skilled in the art will readily conceive of a computer program product that can be derived from the aforementioned method for determining the quantity and price impact of flexible resource response on the electricity spot market. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded to enable a processor to implement various aspects of this disclosure. That is, this application also includes a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the aforementioned method for determining the quantity and price impact of flexible resource response on the electricity spot market.

[0264] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0265] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0266] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0267] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for determining the impact of flexible resource response on the quantity and price of electricity in the spot market, characterized in that, Includes the following steps: Step 1: Quantitatively analyze the adjustability of flexible resources based on the equipment response characteristics of flexible resources, construct a dynamic response model of flexible resources with multiple time scales, evaluate the adjustability performance of flexible resources, and introduce availability coefficients to aggregate flexible resources. Step 2: Based on the results of flexible resource aggregation and combined with the rules of electricity spot market trading, construct a market game model that considers the participation of flexible resources, and solve the model using the backward induction method; Step 3: Combine the flexible resource dynamic response model with the market game model to establish a coupling model between the electricity spot market and the flexible resource response, and obtain the quantitative relationship between the flexible resource response and the electricity spot market price; The quantitative analysis of the adjustability of flexible resources based on the equipment response characteristics further includes: The adjustability of flexible resources is quantitatively analyzed based on the response time of flexible resource devices and the adjustable power range of flexible resources at different time scale levels. Regarding the response time of flexible resource devices, based on the response speed and self-adjustment capability of the flexible resource devices, flexible resources are divided into: second-level, minute-level, and hour-level; the device response time is: In the formula, , , The first i The response time, total configuration capacity, and adjustable power limit per unit time of flexible resource-like equipment; The adjustable power range of the flexible resources is: In the formula, For flexible resource equipment i Adjustable power range; , These are the upper limit output coefficient and lower limit output coefficient of flexible resource equipment, respectively. The construction of a multi-timescale flexible resource dynamic response model further includes: For second-level response, calculate the real-time charge and discharge power of electrochemical energy storage. : In the formula, , These are the energy storage charging power and the discharging power, respectively. , , These are real-time electricity price, off-peak electricity price, and peak electricity price, respectively. For minute-level response, calculate the real-time power adjustment of the air conditioner. : In the formula The temperature control elasticity coefficient; This is the base power for the air conditioning load; The market benchmark electricity price; Calculate the real-time output power of an air source heat pump : In the formula, This is the maximum output power of the heat pump; This is the maximum output power of the CCHP (Combined Gas Harmonized System). The threshold for electricity price for full-load operation of the heat pump; The electricity price sensitivity coefficient for air source heat pumps; For responses lasting hours or longer: Calculate the real-time output power of natural gas combined cooling, heating, and power (CCHP). : In the formula, This is the minimum output power for a combined cooling, heating, and power system for natural gas. Calculate the real-time output power of the cold / heat storage device : In the formula, , These refer to the energy storage capacity and energy release capacity of the cold / heat storage device, respectively. Calculate the power consumption of renewable energy output : In the formula, Forecasted output for renewable energy equipment; The price elasticity coefficient for renewable energy; This refers to the amount of renewable energy that is abandoned due to grid absorption capacity limitations.

2. The method for determining the impact of flexible resource response on the quantity and price of electricity in the spot market according to claim 1, characterized in that, The assessment of the adjustability of flexible resources further includes: Construct an equivalent model for the aggregated cluster: In the formula, as a flexible resource aggregator t The power can always be adjusted up or down; For the first i The availability coefficient of flexible resources; For the first i Flexible resources t Adjustable power up / down at any time ; This refers to the aggregated loss of flexible resources; N This represents the total number of flexible resource equipment types. For assessing the regulation potential, the response speed is calculated. In the formula, For time intervals; For the first i Flexible resource equipment in t Power output at any given moment; For the first i Flexible resource equipment in Power output at any given moment; The adjustment potential index was normalized: In the formula, To adjust the potential normalization value, ; , These represent the maximum and minimum values ​​of the flexible resource adjustment potential, respectively. Calculate adjustment accuracy : In the formula, X To adjust the number of sampling points within the adjustment period; For the first i Flexible resource devices at all times t Target regulating power; The adjustment accuracy index is normalized: In the formula, To adjust the accuracy normalization value, ; , These represent the maximum and minimum values ​​for flexible resource adjustment accuracy, respectively. Calculate the flexibility index : In the formula, K For time scale levels; For the first k Weights for each time scale; , The first i The upward and downward adjustable power of flexible resource-type equipment; For the first i Rated power of flexible resource equipment; The flexibility index is normalized: In the formula, This is a normalized value for the flexibility index. ; , These represent the maximum and minimum values ​​of the flexibility index for flexible resources, respectively. Based on the aforementioned adjustable performance indicators of flexible resources, the dynamic availability coefficient of flexible resources is quantified. Under different scenarios, the availability coefficient of flexible resources changes dynamically according to the scenario. The quantification method is as follows: In the formula, This refers to the dynamic availability coefficient of flexible resources. , , They are respectively t Weighting coefficients for adjustment potential, adjustment accuracy, and flexibility indicators in real-time scenarios.

3. The method for determining the impact of flexible resource response on the quantity and price of electricity in the spot market according to claim 2, characterized in that, The construction of the market game model that considers the participation of flexible resources further includes: Constructing a day-ahead clearing electricity price model with flexible resource participation based on electricity spot market trading rules: In the formula, The electricity price was cleared out a few days ago; For conventional units j The reported electricity volume of the day before; For flexible resource equipment i The reported electricity volume of the day before; For conventional units j The electricity price declared before the date; For flexible resource equipment i The electricity price declared before the date; M This refers to the total number of conventional unit types submitted for approval recently. Based on the day-ahead clearing electricity price model, a master-slave game equilibrium model with flexible resource aggregation participation is constructed, including an upper-level leader optimization model and a lower-level follower optimization model. The objective function of the upper-level leader optimization model is: In the formula, To adjust the total cost of the system; T The duration of the scheduling cycle; To adjust costs for flexible resources; This is the penalty coefficient for electricity price fluctuations; For the reference electricity price in the spot market, either the historical average or the policy target price can be used. Real-time status of energy storage devices; The objective function of the lower-level follower optimization model is: In the formula, For the total revenue of flexible resource adjustment; This refers to the operating costs of flexible resources.

4. The method for determining the impact of flexible resource response on the quantity and price of electricity in the spot market according to claim 3, characterized in that, The method of solving the model using backward induction further includes: For the problem of solving a two-layer master-slave game model in which the upper-layer entity is a market operator with the goal of minimizing the total system cost, and the lower-layer entity is a flexible resource aggregator with the goal of maximizing adjustment revenue, a backward induction method based on multiple initial points and hot start is used to solve the game between the upper and lower layers and obtain the optimal equilibrium solution. In the multi-initial-point sampling stage, the initial electricity price is selected based on a random sampling method, i.e., within the price range. Intrinsic generation R Group initial electricity price curve; In the formula, To initialize the electricity price; It is a uniform sampling function; , These are the minimum allowable electricity price and the maximum allowable electricity price, respectively. The warm start strategy is as follows: In a certain iteration, the optimization functions of the upper and lower layers change gradually, and the current solution is retained as a candidate for a warm start; in parallel computing, the warm start solution is added to the initial point set, replacing threads with slower convergence; according to t Optimal solution for the time period, calculation t The optimal solution for the +1 time period is used as the initialization variable for hot start. In the formula, for t The optimal solution for the given time period; for t +1 is the optimal solution for the time period; This is a correction term based on electricity price trends; In parallel inverse inductive solution, multiple upper and lower-level optimizations based on the initial point are performed simultaneously: An initial electricity price is given during initialization. Set the maximum number of iterations. Q Convergence threshold ; Based on the given initial electricity price, the lower-level optimization model is solved to derive the lower-level response strategy. ; Based on the cost function and inequality constraints, a Lagrangian function is constructed. Then, the derivative with respect to the aggregator adjustment is taken and set to zero to obtain the lower-level optimal solution. In the formula, e , s These are the unit adjustment cost and the marginal cost coefficient, respectively. According to the lower-level response strategy The upper levels adjust electricity prices to minimize total regulation costs; Updating electricity prices based on gradient descent: In the formula, , The first r The initial point is at the _ ... q The next iteration, the... q The electricity price for +1 iteration; The projection operator ensures that the electricity price remains within the limit. This is the gradient descent step size; The convergence criterion is: When the above conditions are met, save the equilibrium solution at this time. Otherwise, continue iterating until the required number of iterations is reached.

5. The method for determining the impact of flexible resource response on the quantity and price of electricity in the spot market according to claim 4, characterized in that, Step 3 further includes: The electricity spot market price after flexible resource response is calculated based on the equilibrium solution of the master-slave game model. The flexible resource equipment in the integrated energy system adjusts its output according to the dispatch instructions issued by the dispatch center. The system dispatch instructions are generated based on the spot market electricity price signal. During peak electricity price periods, generator sets and energy storage equipment increase output to meet load demand and reduce electricity prices. During off-peak electricity price periods, generator sets reduce output, energy storage equipment increases charging power, and the load is shifted to this period. The electricity spot market price signal guides the flexible resource equipment in the integrated energy system to adjust its output. At the same time, after responding to the dispatch instructions, the flexible resource equipment changes its operating status, thereby affecting the spot market electricity price. The final equilibrium solution obtained from the above master-slave game model The flexible resource aggregator issues adjustment commands to the flexible resource equipment. After responding to the commands, the flexible resources will change their output, thereby affecting the electricity supply and demand relationship and thus adjusting the electricity spot market price. The adjustment mechanism is as follows: In the formula, The revised spot market electricity price; for t System load at any given time; The slope of the marginal cost curve for power generation; This study analyzes the coupling relationship between flexible resources and electricity prices, using the price elasticity coefficient to reflect the sensitivity of flexible resource responses to changes in electricity prices. In the formula, This represents the change in the aggregation power of flexible resources. This represents the change in spot market electricity prices; Analyze the impact of flexible resource equipment's power adjustment in one time period on electricity prices in another time period, calculate its sensitivity coefficient, and construct a sensitivity matrix: In the formula, For the first i Flexible resources t Power regulation during time periods t a The impact of time-of-use electricity pricing; for t a Market electricity prices during certain time periods.

6. A system for determining the impact of flexible resource response on the quantity and price of electricity in the spot market, characterized in that, include: The flexible resource adjustability analysis module is used to quantitatively analyze the adjustability of flexible resources based on the equipment response characteristics of flexible resources, construct a dynamic response model of flexible resources at multiple time scales, evaluate the adjustability performance of flexible resources, and introduce availability coefficients to aggregate flexible resources. The market game model construction module is used to construct a market game model that considers the participation of flexible resources based on the aggregation results of flexible resources and combined with the trading rules of the electricity spot market, and to solve the model using the backward induction method. The electricity spot market price impact module is used to combine the flexible resource dynamic response model with the market game model to establish a coupled model of the electricity spot market and flexible resource response, and obtain the quantitative relationship between flexible resource response and electricity spot market price. The quantitative analysis of the adjustability of flexible resources based on the equipment response characteristics further includes: The adjustability of flexible resources is quantitatively analyzed based on the response time of flexible resource devices and the adjustable power range of flexible resources at different time scale levels. Regarding the response time of flexible resource devices, based on the response speed and self-adjustment capability of the flexible resource devices, flexible resources are divided into: second-level, minute-level, and hour-level; the device response time is: In the formula, , , The first i The response time, total configuration capacity, and adjustable power limit per unit time of flexible resource-like equipment; The adjustable power range of the flexible resources is: In the formula, For flexible resource equipment i Adjustable power range; , These are the upper limit output coefficient and lower limit output coefficient of flexible resource equipment, respectively. The construction of a multi-timescale flexible resource dynamic response model further includes: For second-level response, calculate the real-time charge and discharge power of electrochemical energy storage. : In the formula, , These are the energy storage charging power and the discharging power, respectively. , , These are real-time electricity price, off-peak electricity price, and peak electricity price, respectively. For minute-level response, calculate the real-time power adjustment of the air conditioner. : In the formula The temperature control elasticity coefficient; This is the base power for the air conditioning load; The market benchmark electricity price; Calculate the real-time output power of an air source heat pump : In the formula, This is the maximum output power of the heat pump; This is the maximum output power of the CCHP (Combined Gas Harmonized System). The threshold for electricity price for full-load operation of the heat pump; The electricity price sensitivity coefficient for air source heat pumps; For responses lasting hours or longer: Calculate the real-time output power of natural gas combined cooling, heating, and power (CCHP). : In the formula, This is the minimum output power for a combined cooling, heating, and power system for natural gas. Calculate the real-time output power of the cold / heat storage device : In the formula, , These refer to the energy storage capacity and energy release capacity of the cold / heat storage device, respectively. Calculate the power consumption of renewable energy output : In the formula, Forecasted output for renewable energy equipment; The price elasticity coefficient for renewable energy; This refers to the amount of renewable energy that is abandoned due to grid absorption capacity limitations.

7. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method for determining the quantity and price impact of flexible resource response on the electricity spot market according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method for determining the quantity and price impact of flexible resource response on the electricity spot market as described in any one of claims 1-5.

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