Electricity market price risk management method based on weather derivatives

Abstract

The invention discloses a power market price risk management method based on weather derivatives, belongs to a power generator decision-making and electricity price simulation technology, and aims to couple an expected utility maximization target with a power market clearing model to perform power generator decision-making and electricity price simulation. And using a CVaR index to measure the electricity price risk and evaluate the effect of the weather derivative. The effectiveness of the weather derivatives is verified through comparative analysis of a power generator quotation strategy, expected utility, node electricity price and a VaR / CVaR index.

Description

Technical Field

[0001] This invention belongs to the field of power producer decision-making and electricity price simulation technology, and specifically relates to a method for managing power market price risk based on weather derivatives. Aiming at the impact of extreme weather scenarios on the power market, this invention designs a power weather derivative based on electricity price and power generation loss capacity, and couples the expected utility maximization objective with the power market clearing model to perform power producer decision-making and electricity price simulation. VaR Indicators measure electricity price risk and assess the effectiveness of weather derivatives. Background Art

[0002] Building a new power system has become an inevitable trend. The increasing share of renewable energy sources like wind and solar, the increasing complexity and diversity of power loads, and the impact of extreme weather conditions will further exacerbate uncertainties in power supply, load, and transmission line capacity, posing significant challenges to managing electricity price risks in the power market. Consequently, market-based approaches are necessary to manage price risks. From a high-level design perspective, incentivizing power generation and grid companies to directly mitigate price risks during extreme weather events is particularly crucial.

[0003] The impact of extreme weather on electricity prices and the development of weather derivatives as financial instruments to mitigate extreme weather risks have garnered widespread attention. While research on the impact of extreme weather on power systems and electricity prices has primarily focused on three key areas: generation capacity, electricity demand, and transmission capacity, most studies fail to comprehensively consider these three dimensions.

[0004] Research on weather derivatives is currently focused on industries significantly impacted by weather, such as agriculture and energy. Power weather derivatives are a type of power financial instrument, primarily designed to help power market participants mitigate the risks induced by weather factors and reduce their impact on returns. Unlike common power financial instruments such as medium- and long-term power contracts and financial transmission rights, power weather derivatives are not based on electricity or electricity itself, but rather on weather factors such as temperature.

[0005] However, in terms of the impact of extreme weather on power systems and electricity prices, existing literature mostly analyzes the impact of extreme weather on power systems and electricity prices from a single perspective, and there are still certain gaps in multi-perspective comprehensive research; in terms of power weather derivatives, existing literature provides certain weather hedging solutions, but there is no relevant research that combines them with power producers' bidding strategies and considers the impact of weather derivatives on electricity prices themselves.

[0006] While widespread extreme weather events that threaten the safe and stable operation of the power grid and reliable electricity supply will trigger market suspensions, thereby ensuring energy security during these extreme weather events and protecting users from extremely high electricity prices, localized extreme weather events that don't threaten power supply and the safe operation of the grid and don't trigger market suspension conditions still present electricity price risks. In such situations, how to use market mechanisms to curb price increases and manage electricity price risks has become a pressing issue. Summary of the Invention

[0007] In view of the background technology and existing deficiencies, the present invention provides a method for managing electricity market price risks based on weather derivatives. The method aims to comprehensively consider the impact of extreme weather on three aspects: power generation capacity, power demand and transmission capacity. In combination with the market electricity price formation process, the method uses the power generator's bidding decision as an intermediate variable to derive the impact of extreme weather on electricity prices.

[0008] To achieve the above objectives, the present invention provides a method for managing electricity market price risks based on weather derivatives:

[0009] (1) The price factor in the profit function is replaced by a price factor that includes the execution condition and is expressed as follows:

[0010]

[0011] When the actual price is lower than the execution price λ ref When A is equal to the payout price λ w , otherwise, the value of A is 0;

[0012] (2) Adding the loss capacity factor to the profit function to avoid the risk of capacity loss of power generators;

[0013] (3) Taking wind speed and temperature as execution conditions, it is expressed as formula (2):

[0014]

[0015] When the actual temperature is lower than the execution temperature T ref And the actual wind speed is higher than the execution wind speed W ref When , the value of B is 1, otherwise, the value of B is 0;

[0016] The assumed derivatives profit function is expressed as formula (3):

[0017] Φ(T(t),W(t),λ i )=P i loss ·A·B………………(3)

[0018] Where: ref 、T ref and Wref are the electricity price, temperature and wind speed which are pre-set as execution conditions; i is the actual electricity price; w is the compensation price; T(t) and W(t) are the actual temperature and wind speed; P i loss Power generation capacity loss caused by extreme weather.

[0019] Preferably, the owner of the weather derivative can only obtain benefits when the three execution conditions of electricity price, temperature and wind speed are all met, and the net benefit is the difference between the derivative benefit and its price; when the execution conditions are not met, the derivative price is negative.

[0020] Preferably, the impact of extreme weather on source-side factors is expressed by the following formula:

[0021] (1) Thermal power: The power factor of thermal power is mainly affected by the ambient temperature and is expressed by the following formula (4):

[0022]

[0023] Where: C F,tem is the thermal power factor; ρ tem is the thermal power efficiency loss rate; T tem is the external ambient temperature of the thermal power unit; T a The maximum temperature suitable for the operation of thermal power units;

[0024] (2) Photovoltaic: The power factor of photovoltaic is affected by light and temperature and is expressed by the following formula (5):

[0025]

[0026] Where: C F,pv is the photovoltaic power factor; ρ pv is the influence of temperature on photovoltaic power factor; T cell and T a,pv are PV module temperature and standard temperature respectively; r sds and r sds,ref are local irradiation intensity and standard irradiation intensity respectively;

[0027] (3) Wind power: The power factor of wind power is mainly affected by wind speed and is expressed by the following formula (6), where C F,w When it is 0, it means that all fans are off the grid;

[0028]

[0029] Where: C F,w is the power factor of wind power; V0, V r , V1 and V HThey are cut-out wind speed, rated wind speed, cut-in wind speed and actual wind speed.

[0030] Preferably, the impact of extreme weather on load-side factors is reflected as the impact of the load-side on electricity demand, which is expressed by the following formula (7):

[0031] D=D0·(1+δ·ΔT)………………(7)

[0032] Where: D is the user load; D0 is the basic load; δ is the load change amplitude caused by unit temperature change.

[0033] Preferably, the impact of extreme weather on grid-side factors is reflected in the impact of the grid-side on the transmission capacity. The failure rate of transmission lines tripping or shutting down due to factors such as icing and strong winds is expressed by the following formulas (8) and (9):

[0034]

[0035] Where: P f is the line failure rate; L W is the ice wind load; a W is the first threshold of ice wind load; b W is the second threshold of ice wind load; p ij is the failure probability of line ij; l ij is the length of line ij.

[0036] Preferably, common weather derivatives are options with cooling days and heating days as underlying assets, which are expressed by the following formula (10):

[0037]

[0038] A call option means that when the number of heating days exceeds a certain strike value, the option owner can obtain a certain profit; a put option means that when the number of heating days is lower than a certain strike value, the option owner can obtain a certain profit. The profit function is expressed by the following formula (11):

[0039]

[0040] Where: T ref is the reference temperature; T L (t) is the average temperature at L on day t; λ is the factor that converts HDD into monetary value; K is the execution value in days; Φ(H DD,T,L ) represents the payoff of the option.

[0041] Preferably, the impact of extreme weather on power producers is reflected as a bidding function, which is expressed by the following formula (12):

[0042] B i (P i)=k i (αP i 2 +βP i )+C 0,i ………………(12)

[0043] Using C VaR The indicator measures the risk of electricity prices and evaluates the effect of weather derivatives, which is expressed as the following formula (13):

[0044]

[0045] The generator target is expressed by the following formula (14):

[0046]

[0047] Where: E(π i ∣k i ) is the expected profit of the generator when the bid premium factor is k; Z i (k i ) is the utility function of the power producer based on expected revenue and risk; k i is the premium factor of the power generator's quotation; b is the risk attitude; B i For quotation; C 0,i is the fixed cost; P i is the power generation; a is the confidence level; J is the total possible number of events; f(S) is the Sth possible expected loss, which is specifically expressed as the difference from the mean.

[0048] This paper designs a power weather derivative based on electricity price and power generation loss capacity, and analyzes its effectiveness from the theoretical perspective of the benefit function. Finally, the expected utility maximization objective and the power market clearing model are coupled to simulate the power generator decision and electricity price, and the C VaR Indicators measure electricity price risk and assess the effectiveness of weather derivatives.

[0049] The technical solution of the present invention is to analyze the bidding strategy, expected utility, node electricity price and V aR / C VaR The comparative analysis of indicators verifies the effectiveness of weather derivatives. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the effect of extreme weather on market electricity prices involved in the method of the present invention;

[0051] Figure 2 This is a schematic diagram of the net payoff of a call option using heating days (HDD) as the underlying asset according to the method of the present invention;

[0052] Figure 3A schematic diagram of the net income of the weather derivatives of the method of the present invention when the exercise conditions are not met;

[0053] Figure 4 A schematic diagram of the net income of the weather derivatives of the method of the present invention when the exercise conditions are met; DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] The terms "including" and "having" and any variations thereof in the description and claims of the present invention are intended to cover non-exclusive inclusions. For example, a method or product that includes a series of technical features is not necessarily limited to those technical features clearly listed, and may also include other technical features that are not clearly listed and can be included in the method or product.

[0056] The present invention is described in detail below with reference to specific embodiments and accompanying drawings.

[0057] (1) The impact of extreme weather on electricity prices

[0058] The characteristics of electric energy such as difficulty in large-scale storage and immediate use determine that its price is not only determined by supply and demand, but also by the grid architecture, especially under the node electricity price mechanism system commonly used in the current power market. In addition, the quotation of power generators is also one of the important factors affecting the level of electricity prices. The current electricity price is mainly affected by two factors: 1) objective factors on the source, grid and load side: power generation capacity, load level and grid transmission capacity; 2) subjective factors such as power generator quotation decision. Therefore, based on the electricity price formation process, the present invention combines the objective factors on the source, grid and load side and the subjective factor of power generator quotation decision to analyze the effect of extreme weather on electricity prices. Specifically, the main path of the source side's influence on electricity prices is: extreme weather affects the available power generation capacity of power generators, and power generators adjust their quotation strategies based on the electricity price, their own capacity, supply and demand, and grid transmission capacity. The quotation strategy ultimately affects the market clearing electricity price. The main path of the grid side's influence on electricity prices is: extreme weather affects the grid transmission capacity. This capacity participates in the power market clearing and power generator decision-making process in the form of a constraint condition, affecting the node electricity price and the difference between the node electricity prices, and then acting on the node load and power generators. The main impact of the load side on electricity prices is as follows: extreme weather affects electricity demand, which in turn changes the supply and demand structure. Changes in the supply and demand structure directly affect market clearing on the one hand, and affect the quotations of power generators on the other hand. Ultimately, both impacts affect electricity prices.

[0059] In summary, the logic of the electricity price formation process and the path of extreme weather effects on it are summarized in this paper. Figure 1 As shown, the objective factors and subjective factors are represented by red dotted boxes and blue dotted boxes respectively, and the impact path of extreme weather on electricity prices is marked by red and blue arrows.

[0060] (2) Impact of extreme weather on source-grid-load factors

[0061] 1) Source side: Impact on the generating capacity of various power sources. Different power sources are affected by weather to varying degrees. This report summarizes the impact of major weather factors (wind, temperature) on power generation capacity, specifically expressed as power factor, which is the ratio of available capacity to installed capacity.

[0062] Thermal power: The power factor of thermal power is mainly affected by the ambient temperature and is expressed by the following formula (4):

[0063]

[0064] Where: C F,tem is the thermal power factor; ρ tem is the thermal power efficiency loss rate; T tem is the external ambient temperature of the thermal power unit; T a It is the highest temperature suitable for the operation of thermal power units.

[0065] (2) Photovoltaic: The power factor of photovoltaic is affected by light and temperature and is expressed by the following formula (5):

[0066]

[0067] Where: C F,pv is the photovoltaic power factor; ρ pv is the influence of temperature on photovoltaic power factor; T cell and T a,pv are PV module temperature and standard temperature respectively; r sds and r sds,ref are local irradiation intensity and standard irradiation intensity respectively;

[0068] (3) Wind power: The power factor of wind power is mainly affected by wind speed and is expressed by the following formula (6), where C F,w When it is 0, it means that all fans are off the grid;

[0069]

[0070] Where: C F,w is the power factor of wind power; V0, V r , V1 and V H They are cut-out wind speed, rated wind speed, cut-in wind speed and actual wind speed.

[0071] 2) Load side: impact on electricity demand.

[0072] The explanatory variables of electricity demand involve income, electricity price, temperature and other aspects. This paper focuses on the impact of temperature, which is expressed by the following formula (7):

[0073] D=D0·(1+δ·ΔT)………………(7)

[0074] Where: D is the user load; D0 is the basic load; δ is the load change amplitude caused by unit temperature change.

[0075] 3) Grid side: impact on power transmission capacity.

[0076] Transmission lines may trip or shut down due to factors such as icing and strong winds. The failure rate is expressed by the following formula (8)(9):

[0077]

[0078] Where: P f is the line failure rate; L W is the ice wind load; a W is the first threshold of ice wind load; b W is the second threshold of ice wind load; p ij is the failure probability of line ij; l ij is the length of line ij.

[0079] (3) Weather derivative product design

[0080] In order to help the power generation side avoid risks and reduce its willingness to bid high, so as to achieve the goal of reducing the overall market electricity price risk, this paper first designs weather derivatives based on electricity price and power generation loss capacity, and analyzes the impact of weather derivatives on power generators' income from the perspective of profit function, and verifies its role in electricity price risk management from a theoretical level.

[0081] Common weather derivatives are options based on cooling degree days (CDD) and heating degree days (HDD). HDD refers to the number of days with temperatures below the baseline temperature and is calculated as shown in Equation (10). A call option entitles the option holder to a certain return when HDD exceeds a certain strike value; a put option entitles the option holder to a certain return when HDD falls below a certain strike value. The specific payoff function is shown in Equation (11).

[0082]

[0083] Where: T refis the reference temperature; T L (t) is the average temperature at L on day t; λ is the factor that converts HDD into monetary value; K is the execution value in days; Φ(H DD,T,L ) represents the payoff of the option.

[0084] The option owner needs to pay a certain fee to obtain the option, which is the price of the option. Taking the call option as an example, according to formula (11), when HDD is lower than the strike value, the payoff of the call option is 0, and the net payoff of the option is the negative option price; when HDD is higher than the strike value, the net payoff is the difference between the option payoff and the option price. Therefore, the net payoff of the HDD call option is as follows: Figure 2 shown.

[0085] The main improvements of the weather derivatives designed by the present invention are:

[0086] 1) Change the price factor in the revenue function (λ in Equation (11)) to a price factor that includes the execution condition (Equation (1)). When the actual price is lower than the execution price λ ref When A is equal to the payout price λ w , otherwise, the value of A is 0.

[0087] 2) Add the loss capacity factor to the profit function to avoid the risk brought by the capacity loss of power generators.

[0088] 3) Since extreme weather cannot be directly measured by CDD and HDD, the present invention uses wind speed and temperature indicators as execution conditions. Taking winter ultra-low temperature as an example, the weather execution conditions are shown in formula (2).

[0089] When the actual temperature is lower than the execution temperature T ref And the actual wind speed is higher than the execution wind speed W ref When , B takes the value of 1, otherwise, B takes the value of 0. Finally, the derivative income function set in this paper is shown in formula (3).

[0090]

[0091] Φ(T(t),W(t),λ i )=P i loss ·A·B………………(3)

[0092] Where: ref 、T ref and W ref are the electricity price, temperature and wind speed which are pre-set as execution conditions; i is the actual electricity price; w is the compensation price; T(t) and W(t) are the actual temperature and wind speed; Pi loss Power generation capacity loss caused by extreme weather.

[0093] When the three execution conditions of electricity price, temperature and wind speed are all met, the owner of weather derivatives can obtain benefits. At this time, the relationship between the net income of derivatives and the loss capacity of power producers is as follows: Figure 4 As shown, the net income is the difference between the derivative income and its price; when the execution conditions are not met, the relationship is as follows Figure 3 As shown, this is a negative derivative price.

[0094] (4) Impact of extreme weather on power generators

[0095] Extreme weather affects the basic market elements such as power generation capacity, power demand, and transmission capacity, which will further trigger changes in the decision-making of market players. Under extreme weather conditions, power generators will adjust their spot market quotes after predicting the market status. Assuming that each power generator is risk-averse and aims to maximize expected utility, they use a cost-plus method to quote. Their quote function is shown in Equation (12). Existing literature often uses V aR or C VaR Refers to indicators to measure risk, where V aR It is used to measure the maximum loss within the confidence range. Its advantages are simple and intuitive concepts and convenient calculations, but its disadvantage is that it cannot estimate extreme risks beyond the confidence range. VaR The measurement is over V aR The average of the excess losses of the value part can better measure the extreme risk situation. Therefore, in order to better measure the impact of low-probability and high-risk extreme weather events, this paper adopts the mean C VaR The magnitude of the risk is shown in Equation (13). The generator's target is shown in Equation (14).

[0096] B i (P i )=k i (αP i 2 +βP i )+C 0,i ………………(12)

[0097]

[0098] Where: E(π i ∣k i ) is the expected profit of the generator when the bid premium factor is k; Z i (k i ) is the utility function of the power producer based on expected revenue and risk; k i is the premium factor of the power generator's quotation; b is the risk attitude; B i For quotation; C0,i is the fixed cost; P i is the power generation; a is the confidence level; J is the total possible number of events; f(S) is the Sth possible expected loss, which is specifically expressed as the difference from the mean.

[0099] Through this risk assessment method, the changes in electricity price risks under different strategies can be effectively evaluated, and the risk avoidance effect of the weather derivatives designed by the present invention can be verified.

[0100] The technical solution of this application verifies the effectiveness of weather derivatives through comparative analysis of power generators' bidding strategies, expected utility, node electricity prices, and VaR / CVaR indicators:

[0101] The risk mitigation effect for power generators: After using weather derivatives, the maximum impact of extreme weather events decreased from 35.07% to 6.76%, a reduction of 80.98%. From a CVaR perspective, weather derivatives can effectively reduce extreme risks for power generators.

[0102] Electricity price control effect: After using weather derivatives, the average electricity price at the unified settlement point decreased by 0.21% to 0.22%, CVaR decreased by 20.82% to 35.93%, and electricity price volatility decreased by 21.26% to 24.51%.

[0103] Experimental results demonstrate that the weather derivatives designed in this invention can effectively reduce the losses of power generators during extreme weather events and their willingness to bid high, thereby lowering extreme weather electricity prices and controlling extreme weather price risks. The significant improvement in the CVaR indicator demonstrates the method's ability to effectively manage extreme risks.

Claims

1. A method for managing electricity market price risk based on weather derivatives, characterized by: (1) The price factor in the profit function is replaced by a price factor that includes the execution condition and is expressed as follows: When the actual price is lower than the execution price λ ref When A is equal to the payout price λ w , otherwise, the value of A is 0; (2) Adding the loss capacity factor to the profit function to avoid the risk of capacity loss of power generators; (3) Taking wind speed and temperature as execution conditions, it is expressed as formula (2): When the actual temperature is lower than the execution temperature T ref And the actual wind speed is higher than the execution wind speed W ref When , the value of B is 1, otherwise, the value of B is 0; The assumed derivatives profit function is expressed as formula (3): Φ(T(t),W(t),λ i )=P i loss ·A·B………………(3) Where: ref 、T ref and W ref are the electricity price, temperature and wind speed which are pre-set as execution conditions; i is the actual electricity price; w is the compensation price; T(t) and W(t) are the actual temperature and wind speed; P i loss Power generation capacity loss caused by extreme weather.

2. The method for managing electricity market price risk based on weather derivatives according to claim 1, wherein: Only when the three execution conditions of electricity price, temperature and wind speed are met can the owner of weather derivatives obtain profits. The net profit is the difference between the derivative profit and its price. When the execution conditions are not met, the derivative price is negative.

3. The method for managing electricity market price risk based on weather derivatives according to claim 1, wherein: The impact of extreme weather on source-side factors is expressed by the following formula: (1) Thermal power: The power factor of thermal power is mainly affected by the ambient temperature and is expressed by the following formula (4): Where: C F,tem is the thermal power factor; ρ tem is the thermal power efficiency loss rate; T tem is the external ambient temperature of the thermal power unit; T a The maximum temperature suitable for the operation of thermal power units; (2) Photovoltaic: The power factor of photovoltaic is affected by light and temperature and is expressed by the following formula (5): Where: C F,pv is the photovoltaic power factor; ρ pv is the influence of temperature on photovoltaic power factor; T cell and T a,pv are PV module temperature and standard temperature respectively; r sds and r sds,ref are local irradiation intensity and standard irradiation intensity respectively; (3) Wind power: The power factor of wind power is mainly affected by wind speed and is expressed by the following formula (6), where C F,w When it is 0, it means that all fans are off the grid; Where: C F,w is the power factor of wind power; V0, V r , V1 and V H They are cut-out wind speed, rated wind speed, cut-in wind speed and actual wind speed.

4. The method for managing electricity market price risk based on weather derivatives according to claim 1, wherein: The impact of extreme weather on load-side factors is reflected in the impact of the load-side on electricity demand, which is expressed by the following formula (7): D=D0·(1+δ·ΔT)………………(7) Where: D is the user load; D0 is the basic load; δ is the load change amplitude caused by unit temperature change.

5. The method for managing electricity market price risk based on weather derivatives according to claim 1, wherein: The impact of extreme weather on grid-side factors is reflected in the impact of the grid-side on transmission capacity. Transmission lines trip or shut down due to factors such as icing and strong winds, and their failure rates are expressed by the following formulas (8) and (9): Where: P f is the line failure rate; L W is the ice wind load; a W is the first threshold of ice wind load; b W is the second threshold of ice wind load; p ij is the failure probability of line ij; l ij is the length of line ij.

6. The method for managing electricity market price risk based on weather derivatives according to claim 1, wherein: Common weather derivatives are options with cooling days and heating days as underlying assets, which are expressed by the following formula (10): A call option means that when the number of heating days exceeds a certain strike value, the option owner can obtain a certain profit; a put option means that when the number of heating days is lower than a certain strike value, the option owner can obtain a certain profit. The profit function is expressed by the following formula (11): Where: T ref is the reference temperature; T L (t) is the average temperature at L on day t; λ is the factor that converts HDD into monetary amount; K is the execution value in days; Φ(H DD,T,L ) represents the payoff of the option.

7. The method for managing electricity market price risk based on weather derivatives according to claim 1, characterized in that: The impact of extreme weather on power producers is reflected in the bidding function, which is expressed by the following formula (12): B i (P i )=k i (αP i 2 +βP i )+C 0,i ………………(12) Using C VaR The indicator measures the risk of electricity prices and evaluates the effect of weather derivatives, which is expressed as the following formula (13): The generator target is expressed by the following formula (14): Where: E(π i ∣k i ) is the expected profit of the generator when the bid premium factor is k; Z i (k i ) is the utility function of the power producer based on expected revenue and risk; k i is the premium factor of the power generator's quotation; b is the risk attitude; B i For quotation; C 0,i is the fixed cost; P i is the power generation; a is the confidence level; J is the total possible number of events; f(S) is the Sth possible expected loss, which is specifically expressed as the difference from the mean.

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