Quantum computing-based power option pricing method, device, medium and equipment

Abstract

The present disclosure relates to the technical field of electricity pricing, and provides a quantum computing-based electricity option pricing method, device, medium and equipment. The method comprises: acquiring new energy electricity price data; based on the new energy electricity price data, constructing a random process model of the new energy electricity price, and determining a probability distribution of the electricity price at the option expiration time based on the random process model; based on the probability distribution of the electricity price, determining a quantum state representing the probability distribution; and converting a preset electricity option payment function into a quantum payment operator; based on a quantum amplitude estimation algorithm, applying the quantum payment operator to the quantum state to estimate the expected return of the electricity option; and based on the expected return of the electricity option, determining a target electricity option price. The present embodiment introduces quantum computing technology to improve the calculation efficiency and accuracy of electricity option pricing, and realizes fast and accurate estimation of the expected return of the electricity option.

Description

Technical Field

[0001] This disclosure relates to the field of electricity pricing technology, and more specifically, to a method, apparatus, medium, and equipment for electricity option pricing based on quantum computing. Background Technology

[0002] With the rapid increase in the proportion of new energy sources in the power system, the volatility and uncertainty of electricity market prices have significantly increased. Due to the intermittent and random nature of new energy power generation, spot electricity prices exhibit complex characteristics such as high-frequency fluctuations, nonlinearity, jumps, and long memory. These characteristics increase the risks for market participants in electricity trading, making effective risk management tools increasingly important. Electricity options, as a financial derivative, have the ability to cope with price volatility risk and provide market participants with expected stability. However, the complexity of new energy electricity prices poses a challenge to traditional electricity option pricing models, especially in real-time trading environments where fast and efficient pricing capabilities are essential.

[0003] Currently, pricing methods for new energy power options mainly include numerical methods based on partial differential equations, Monte Carlo simulation methods, and data-driven machine learning methods. While each method has its advantages, they also have significant drawbacks in practical applications: numerical methods based on partial differential equations suffer from low efficiency due to their high computational dimensionality, making it difficult to meet the demands of real-time, rapid pricing; Monte Carlo simulation methods have strong applicability, but their computational cost is high, requiring a large number of samples, thus increasing processing time; machine learning methods rely heavily on large amounts of training data and may have insufficient generalization ability, resulting in unsatisfactory performance in new scenarios. Summary of the Invention

[0004] This disclosure provides at least one method, apparatus, medium, and device for pricing electricity options based on quantum computing. By introducing quantum computing technology, the computational efficiency and accuracy of electricity option pricing are improved, enabling rapid and accurate estimation of the expected returns of electricity options.

[0005] This disclosure provides a quantum computing-based electricity option pricing method, including: Obtain new energy power price data; wherein, the new energy power price data includes historical power price data and at least one new energy prediction factor data; Based on the new energy power price data, a stochastic process model of new energy power price is constructed, and the probability distribution of power price at option expiration time is determined based on the stochastic process model. Based on the probability distribution of the electricity price, a quantum state representing the probability distribution is determined; and a preset electricity option payoff function is transformed into a quantum payoff operator. Based on the quantum amplitude estimation algorithm, the quantum payoff operator is applied to the quantum state to estimate the expected payoff of the power option; and based on the expected payoff of the power option, the target power option price is determined.

[0006] In some possible embodiments, the construction of the stochastic process model for new energy electricity prices includes: Based on the historical electricity price data, the electricity characteristics of new energy electricity prices are modeled to obtain an initial stochastic process model; wherein, the parameters of the initial stochastic process model include drift rate parameter, volatility parameter, and jump strength parameter; The new energy predictor data is input into a pre-trained prediction model to determine the correction values ​​for the drift rate parameter, volatility parameter, and jump intensity parameter; wherein, the new energy predictor data includes at least one of the following data: wind speed data, solar intensity data, and on-grid electricity price prediction data; Based on the correction value, the parameters of the initial stochastic process model are updated to obtain the stochastic process model.

[0007] In some possible embodiments, determining the probability distribution of the electricity price at option expiration based on the stochastic process model includes: Based on the parameters of the stochastic process model, a quantum circuit for simulating the stochastic evolution of electricity prices is constructed. By running the quantum circuit and measuring the output quantum state of the quantum circuit, the probability distribution of the electricity price at the option's expiration time can be obtained.

[0008] In some possible embodiments, determining the quantum state representing the probability distribution based on the electricity price includes: The number of qubits used for encoding is determined based on the number of different price states in the probability distribution. An amplitude encoding method is used to encode the probability amplitude corresponding to each price state in the probability distribution into the computational ground state amplitude represented by the qubit, thereby obtaining a quantum state; wherein, the quantum state is represented as a superposition of multiple computational ground states.

[0009] In some possible embodiments, the conversion of the preset electricity option payment function into a quantum payment operator includes: The payment conditions and computational logic defined by the preset power option payment function are mapped to a sequence of quantum gate operations that perform conditional labeling and phase rotation on quantum states, forming the quantum payment operator.

[0010] In some possible embodiments, the step of applying the quantum payoff operator to the quantum state based on the quantum amplitude estimation algorithm to estimate the expected payoff of the electricity option includes: The quantum payment operator is used to mark the computational ground state in the quantum state that satisfies the payment condition; Based on the quantum amplitude estimation algorithm, the labeled quantum state is processed to estimate the mathematical expectation of the power option payoff function under the probability distribution, and the estimation result is used as the expected payoff of the power option.

[0011] In some possible embodiments, determining the target power option price based on the expected payoff of the power option includes: Obtain the preset risk-free interest rate and option expiration time; The expected return of the power option is discounted based on the risk-free interest rate and the option's expiration time to obtain the target power option price.

[0012] This disclosure provides a quantum computing-based electricity options pricing device, comprising: The data acquisition module is used to acquire new energy power price data; wherein, the new energy power price data includes historical power price data and at least one new energy prediction factor data; The model building module is used to construct a stochastic process model of new energy electricity prices based on the new energy electricity price data, and to determine the probability distribution of electricity prices at the option expiration time based on the stochastic process model. The quantum conversion module is used to determine the quantum state representing the probability distribution of the electricity price based on the probability distribution of the electricity price; and to convert the preset electricity option payoff function into a quantum payoff operator; The price determination module is used to estimate the expected payoff of the power option by applying the quantum payoff operator to the quantum state based on a quantum amplitude estimation algorithm; and to determine the target power option price based on the expected payoff of the power option.

[0013] In some possible embodiments, the model building module is specifically used for: Based on the historical electricity price data, the electricity characteristics of new energy electricity prices are modeled to obtain an initial stochastic process model; wherein, the parameters of the initial stochastic process model include drift rate parameter, volatility parameter, and jump strength parameter; The new energy predictor data is input into a pre-trained prediction model to determine the correction values ​​for the drift rate parameter, volatility parameter, and jump intensity parameter; wherein, the new energy predictor data includes at least one of the following data: wind speed data, solar intensity data, and on-grid electricity price prediction data; Based on the correction value, the parameters of the initial stochastic process model are updated to obtain the stochastic process model.

[0014] In some possible embodiments, the model building module is specifically used for: Based on the parameters of the stochastic process model, a quantum circuit for simulating the stochastic evolution of electricity prices is constructed. By running the quantum circuit and measuring the output quantum state of the quantum circuit, the probability distribution of the electricity price at the option's expiration time can be obtained.

[0015] In some possible embodiments, the quantum conversion module is specifically used for: The number of qubits used for encoding is determined based on the number of different price states in the probability distribution. An amplitude encoding method is used to encode the probability amplitude corresponding to each price state in the probability distribution into the computational ground state amplitude represented by the qubit, thereby obtaining a quantum state; wherein, the quantum state is represented as a superposition of multiple computational ground states.

[0016] In some possible embodiments, the quantum conversion module is specifically used for: The payment conditions and computational logic defined by the preset power option payment function are mapped to a sequence of quantum gate operations that perform conditional labeling and phase rotation on quantum states, forming the quantum payment operator.

[0017] In some possible embodiments, the price determination module is specifically used for: The quantum payment operator is used to mark the computational ground state in the quantum state that satisfies the payment condition; Based on the quantum amplitude estimation algorithm, the labeled quantum state is processed to estimate the mathematical expectation of the power option payoff function under the probability distribution, and the estimation result is used as the expected payoff of the power option.

[0018] In some possible embodiments, the price determination module is specifically used for: Obtain the preset risk-free interest rate and option expiration time; The expected return of the power option is discounted based on the risk-free interest rate and the option's expiration time to obtain the target power option price.

[0019] This disclosure provides a computer device including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform a quantum computing-based electricity option pricing method as described in any of the above possible embodiments.

[0020] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a quantum computing-based electricity option pricing method as described in any of the possible embodiments above.

[0021] The quantum computing-based electricity option pricing method, apparatus, medium, and device provided in this disclosure improve the computational efficiency and accuracy of electricity option pricing by introducing quantum computing technology, combining the stochastic process model of new energy electricity prices with quantum state encoding and quantum payoff operators, and using a quantum amplitude estimation algorithm to estimate expected returns in parallel, thereby achieving rapid and accurate estimation of expected returns for electricity options.

[0022] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0024] Figure 1 A flowchart of a quantum computing-based electricity option pricing method provided in an embodiment of this disclosure is shown; Figure 2 A flowchart of a method for constructing a stochastic process model provided by an embodiment of this disclosure is shown; Figure 3 A flowchart of a quantum state encoding method provided by an embodiment of this disclosure is shown; Figure 4 A schematic diagram of the structure of a quantum computing-based electricity option pricing device provided in an embodiment of this disclosure is shown; Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0027] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0028] To facilitate understanding of this embodiment, the executing entity of the quantum computing-based electricity option pricing method provided in this disclosure will first be described in detail. The executing entity of the quantum computing-based electricity option pricing method provided in this disclosure is a computer device. This computer device can be a terminal device or a server. The terminal device can also be a mobile device, a user terminal, a terminal, a handheld device, a computing device, an in-vehicle device, a wearable device, etc. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of computer devices and servers.

[0029] The quantum computing-based electricity option pricing method provided in this application will be described in detail below with reference to the accompanying drawings. See also: Figure 1 The diagram shows a flowchart of a quantum computing-based electricity option pricing method provided in this embodiment of the present disclosure. The method includes the following steps S101 to S104: S101, obtain new energy power price data.

[0030] It is understandable that new energy power price data refers to various structured or unstructured data sets related to the price formation mechanism of the new energy power market, used to support subsequent modeling of the stochastic process of power prices and option pricing analysis. Specifically, it can include historical power price data and at least one new energy predictive factor data. Among them, historical power price data refers to the actual transaction price sequence recorded in the power spot market over a certain period of time, reflecting the price trajectory under the combined influence of market supply and demand, policy regulation, and seasonality, and can include hourly power prices, daily settlement power prices, regional node power prices, etc. New energy predictive factor data represents observable or predictable parameters that can directly or indirectly reflect the power generation output level of new energy, thereby affecting the power market supply and demand balance and price fluctuations. It can be obtained from sources such as meteorological monitoring systems, numerical weather prediction models, and power market forecasting platforms, and can specifically include at least one of the following data: wind speed data, solar irradiance data, and on-grid electricity price forecast data.

[0031] Here, wind speed data refers to the time series of wind speeds measured or forecasted in a specific region or wind farm, used to represent the availability and power generation potential of wind energy resources; solar irradiance data refers to the time series of solar irradiance measured or forecasted in a specific region or photovoltaic power station, used to represent the output level of photovoltaic power generation; and grid-connected electricity price forecast data refers to the estimated value of electricity trading prices for a certain period in the future based on market rules, policy guidance, and supply and demand analysis, used to represent the market's general expectation of future electricity prices.

[0032] In some other embodiments, the new energy prediction factor data may also include meteorological data such as precipitation, temperature, and cloud cover, as well as power load prediction data, unit maintenance plan information, etc., without specific limitations.

[0033] S102, Based on the new energy power price data, construct a stochastic process model of new energy power price, and determine the probability distribution of power price at option expiration time based on the stochastic process model.

[0034] Specifically, the stochastic process model of new energy electricity prices refers to a dynamic model that mathematically describes the random changes in electricity prices over future time series based on new energy data. It is used to quantitatively characterize the uncertainties such as trends, fluctuations, and jumps in electricity prices. It can be constructed through parameter estimation or machine learning methods to represent the probabilistic structure of the evolution of electricity price status over time.

[0035] For example, because the price of new energy electricity is affected by multiple factors such as the intermittency of renewable energy output, market policy adjustments, and extreme weather events, its dynamic process often exhibits complex characteristics such as nonlinearity, non-stationarity, and sudden jumps. Therefore, when constructing a pricing model that can accurately reflect these characteristics, reference should be made to... Figure 2 As shown, the steps S201~S203 may be included: S201, Based on the historical electricity price data, the electricity characteristics of new energy electricity prices are modeled to obtain an initial stochastic process model.

[0036] Here, the initial stochastic process model refers to the baseline stochastic model obtained solely by fitting historical electricity price statistical characteristics. Its parameters can be calibrated using statistical methods such as maximum likelihood estimation, moment matching, or Bayesian inference. These parameters may include drift rate, volatility, and jump intensity. The drift rate reflects the rate of change in the long-term average trend of electricity prices; the volatility measures the magnitude of random price fluctuations; and the jump intensity describes the average frequency of sudden, large price changes (jumps). In the initial parameter calibration of the initial stochastic process model, taking the Merton jump-diffusion model as an example, the maximum likelihood estimation method can be used to jointly estimate the distribution parameters of drift rate, volatility, jump intensity, and jump magnitude using both continuous returns and extreme volatility information from historical electricity price sequences, thereby completing the quantitative calibration of the initial model.

[0037] Specifically, when modeling the electricity characteristics of new energy electricity prices based on historical electricity price data, the process can include performing stationarity tests, autocorrelation analysis, volatility clustering tests, and extreme value analysis on the historical electricity price series, and then selecting a suitable stochastic process framework and fitting the parameters. Taking the construction of a model that couples jumps and stochastic volatility characteristics as an example, when fitting the parameters, the logarithmic return series of electricity prices can first be subjected to the ADF test to verify its stationarity. Then, the autocorrelation function of the squared returns can be calculated to test the volatility clustering. Next, extreme value theory or kurtosis analysis can be used to identify jump points and estimate jump intensity. Then, the above statistical characteristics can be embedded as constraints into the model calibration process, and finally, an initial stochastic process model that can simultaneously characterize continuous fluctuations in electricity prices, time-varying volatility, and sudden jumps can be obtained.

[0038] In some possible implementations, the Merton Jump Diffusion model can be used to characterize the sudden fluctuations in renewable energy electricity prices, and the Heston Stochastic Volatility Model can be selected to simulate the randomness of electricity price volatility, thus constructing an initial stochastic process model. Here, the Merton Jump Diffusion model is a mathematical model that, based on geometric Brownian motion, introduces a stochastic jump process following a Poisson distribution to describe sudden and significant price changes. It is used to embed intermittent jump risks into the traditional continuous diffusion path, thereby more realistically reflecting the peak characteristics of electricity prices caused by events such as extreme weather or grid failures. The Heston Stochastic Volatility Model is a mathematical model that assumes that the volatility of asset prices itself follows a stochastic process, which can characterize the characteristics of volatility changes over time, i.e., volatility clustering (high volatility is often followed by high volatility, and low volatility is often followed by low volatility). After estimating the basic parameters of the above models or their simplified forms based on historical data, a mathematical framework adapted to the statistical characteristics of renewable energy electricity prices can be provided for model selection, thus obtaining an initial stochastic process model. An exemplary mathematical expression of this model can be expressed as: ; in, This represents the electricity price at time t; This is the drift term, representing the average growth rate of electricity prices; Volatility; Indicates the jump range; Represented as standard Brownian motion, it represents continuous random fluctuations in prices; This refers to the process of cypress jumping.

[0039] S202, input the new energy prediction factor data into the pre-trained prediction model to determine the correction of the drift rate parameter, volatility parameter and jump strength parameter.

[0040] Furthermore, after constructing the initial stochastic process model, since the initial model only reflects historical statistical patterns and does not incorporate real-time or predicted future external driving information, the model parameters can be dynamically adjusted by introducing new energy predictive factor data. That is, based on the expected output of renewable energy and changes in market conditions reflected by the new energy predictive factor data, correction values ​​for parameters such as drift rate, volatility, and jump intensity can be obtained.

[0041] Here, the pre-trained prediction model refers to a machine learning model trained based on historical data that can establish a mapping relationship between new energy prediction factors and electricity price model parameters. It can be used to output the adjustment amount of stochastic process model parameters based on the input prediction data such as wind speed and sunshine.

[0042] In some possible embodiments, Long Short-Term Memory (LSTM), Gradient Boosting Decision Tree, or Neural Network can be used as the pre-trained prediction model to achieve an end-to-end nonlinear mapping from multi-dimensional predictors to multi-model parameters. During the training process, historical data on new energy predictors for the same period can be used as input, with the actual changes in electricity price model parameters estimated through a rolling time window as labels. Supervised learning is then used to train the model, resulting in a trained prediction model that serves as a parameter corrector.

[0043] In some possible embodiments, the pre-trained prediction model can also be built based on probabilistic models such as Bayesian linear regression or Gaussian process regression, without being specifically limited here.

[0044] S203, Update the parameters of the initial stochastic process model according to the correction value to obtain the stochastic process model.

[0045] Specifically, after obtaining the parameter correction values ​​output by the prediction model, the parameters of the initial stochastic process model can be updated by algebraically superimposing or weighted fusion of the correction values ​​with the initial model parameters, thereby obtaining the stochastic process model. The stochastic process model can more accurately reflect the risk characteristics of electricity prices in a specific future period.

[0046] In some other embodiments, the parameters of the stochastic process model can also be dynamically adjusted according to changes in external market environment information and prediction confidence. This may include online correction of the drift rate parameter based on real-time electricity market transaction data, such as load forecast deviation, inter-regional tie-line transmission margin, and unit start-up and shutdown status; or, scenario-adaptive adjustment of the jump intensity parameter based on the confidence level of weather forecasts or extreme weather warning levels; no specific limitations are made here.

[0047] Furthermore, after obtaining the stochastic process model of new energy electricity prices, the possible evolution paths of future electricity prices can be simulated based on this model, thereby obtaining the probability distribution of electricity prices at option expiration. Since the core of option pricing is the quantitative assessment of the price uncertainty of the underlying asset (electricity as referred to in this disclosure) at expiration, the probability distribution of electricity prices at option expiration can be statistically determined by simulating a large number of possible paths. This allows the subsequent steps to transform the financial pricing problem into a mathematical form that can be processed on a quantum computer.

[0048] Specifically, when generating the probability distribution of electricity prices at option expiration based on a stochastic process model, the following steps (1) to (2) may be included: (1) Based on the parameters of the stochastic process model, construct a quantum circuit for simulating the stochastic evolution of electricity prices; (2) Run the quantum circuit and measure the output quantum state of the quantum circuit to obtain the probability distribution of the electricity price at the time of the option's expiration.

[0049] Understandably, a quantum circuit refers to a computational program consisting of a series of quantum logic gates executed in a specific order, which can implement specific algorithms or simulation tasks on a quantum computer. The parameters of a stochastic process model, such as drift rate, volatility, and jump strength, define the mathematical rules governing price evolution. Therefore, based on these parameters, a corresponding quantum circuit can be constructed, and the dynamic evolution of this circuit is equivalent to simulating the electricity price path using a defined stochastic process.

[0050] Furthermore, after constructing and operating the quantum circuit, a quantum state representing the superposition of multiple possible price evolution paths can be obtained. Within this quantum state, a specific set of qubits is dedicated to representing the price information at option expiration. By repeatedly measuring this specific set of qubits, each measurement causes the quantum state to collapse, thus yielding a definite expiration price value. After a sufficient number of measurements, the statistical frequencies of different price values ​​constitute the probability distribution (discrete) of the electricity price at option expiration. Here, the probability distribution obtained through quantum measurement can quantitatively describe the likelihood of future electricity prices falling within various discrete intervals, providing direct data input for subsequently encoding the probability information into a quantum state.

[0051] S103, based on the probability distribution of the electricity price, determine the quantum state representing the probability distribution; and convert the preset electricity option payment function into a quantum payment operator.

[0052] Here, quantum encoding technology can be used to transform the discrete probability distribution data of electricity prices from the current moment to the option's expiration moment into a quantum state that can be stored and processed in a quantum computer. A quantum payment operator is a quantum circuit module or sequence of quantum logic gates designed to perform financial payment function calculations on a quantum state. It can express and implement operations for identifying, labeling, and numerically converting quantum state components that satisfy specific conditions.

[0053] Understandably, after obtaining the probability distribution of electricity prices at option expiration, this probability distribution can be loaded into a quantum register to determine the initial state for subsequent quantum computation. Specifically, when encoding the probability distribution into a quantum state, reference is made to... Figure 3 As shown, the following steps S301~S302 may be included: S301, determine the number of qubits to be encoded based on the number of different price states in the probability distribution.

[0054] Here, the probability distribution includes multiple discrete, mutually exclusive possible price states. According to the principle of binary encoding, to unambiguously represent N different price states, the number of qubits n required must satisfy the following relationship: 2 n ≥N. The number of qubits directly determines the size of the state space that a quantum system can represent, that is, the maximum number of price states that can be encoded. For example, if the probability distribution contains 8 possible electricity price levels, then 3 qubits are needed for encoding.

[0055] S302, using amplitude encoding, the probability amplitude corresponding to each price state in the probability distribution is encoded into the calculated ground state amplitude represented by the quantum bit, thus obtaining the quantum state.

[0056] Furthermore, after determining the required number of qubits, the probability amplitude (i.e., the square root of the probability) of each price state can be allocated to the corresponding computational ground state amplitude of the register. Amplitude encoding refers to an encoding technique that directly stores data in the quantum state amplitude, which can be used to achieve a high-fidelity mapping from classical probability distributions to quantum superposition states.

[0057] For example, during the encoding operation, a series of controlled rotation gates and quantum logic gates can be used to initialize the code. The quantum state is gradually transformed into the target quantum state, which can be mathematically represented as: ,in It represents the probability of the i-th price state occurring. This corresponds to the computational ground state of the binary code representing the price state. Here, the quantum state is a superposition of multiple computational ground states; its physical meaning is that when this quantum state is measured, it collapses back to the ground state. The probability is exactly equal to This allows the information of the original probability distribution to be fully contained.

[0058] Understandably, a predefined payoff function for an electricity option refers to a mathematical expression explicitly specified in a financial contract, used to calculate the option's payoff at expiration, such as the payoff function for a European call option. ; Defines the price of the underlying asset at the option's expiration. The rules for calculating the payment amount with respect to the strike price K. The role of the quantum payment operator is to transform the calculation rules into a series of quantum gate operations, enabling it to process the quantum states that encode the price distribution. Parallel processing is performed, allowing for the simultaneous evaluation of payments under all possible price paths in a single quantum operation.

[0059] This disclosure applies not only to European options but also to American and Asian options, as well as to derivative pricing scenarios such as new energy power swaps and power forward contracts. The payoff function can be defined according to the specific contract terms and payoff structure of the derivative, and is not specifically limited here. For example, American options allow exercise at any time before expiration, and their payoff function needs to incorporate the optimal stop time problem; the payoff of Asian options depends on the average price of the underlying asset over a specific period, and their payoff function involves path averaging calculations. For each derivative, corresponding quantum conditional labeling and payoff conversion logic can be designed based on the specific form of its payoff function, constructing a corresponding quantum payoff operator, thereby achieving fast and accurate valuation within the quantum pricing framework proposed in this disclosure.

[0060] In some possible embodiments, when converting a preset option payoff function into a quantum payoff operator, the payoff conditions and computational logic defined by the preset electricity option payoff function can be mapped to a sequence of quantum gate operations that conditionally label and phase-rotate quantum states, thus forming a quantum payoff operator. Here, the entire conversion process can include two main stages: the first is "condition labeling," which involves constructing quantum logic to identify and label quantum states. Those corresponding price states satisfy the payment conditions (e.g.) The calculation of the ground state components of a superposition, for example, for a European call option, involves the payout condition being that the price of the underlying asset is greater than the strike price. This can be achieved by designing a quantum comparator circuit that operates a qubit register encoding price information along with one or more auxiliary qubits. The quantum comparator can compare and judge each price component in the superposition in parallel, and when a price component satisfies the payout condition, it flips the state of a specific auxiliary qubit (i.e., the tag bit), for example, by changing it from |0 to |10|. Set to |1 Through this process, the original quantum state can be transformed into a new superposition state carrying explicit payment condition marking information. The next step is the "payment transformation," which involves rotating or scaling the amplitude or phase of the quantum state according to the specific payment amount of the marked component. A typical implementation uses controlled rotation operations, designing a series of quantum rotation gates whose rotation angle is related to the calculated payment amount (e.g., ...). The operation of these rotating gates is proportional to the quantum state of the aforementioned flag bit: only when the flag bit is in |1... Only when the state (indicating that the path is in-price) is the corresponding angle rotation operation performed on its associated price component; if the flag bit is |0 (Indicating an out-of-price state), no rotation is performed, and the corresponding payout is zero. Through this series of controlled phase rotations or amplitude scaling operations, the numerical information of the payout function can be effectively encoded into the phase of the final quantum state or the amplitude of a specific subspace. Thus, by sequentially executing the quantum gate sequences corresponding to the condition marking and payout transition stages, a complete quantum circuit module, namely the quantum payout operator, can be constructed. This operator, acting on the initial quantum state, outputs a quantum state containing option payout information.

[0061] S104, based on the quantum amplitude estimation algorithm, the quantum payoff operator is applied to the quantum state to estimate the expected payoff of the power option; and, based on the expected payoff of the power option, the target power option price is determined.

[0062] Understandably, quantum amplitude estimation algorithms are quantum algorithms that utilize quantum phase estimation and quantum interference principles to estimate the amplitude of a specific target in a quantum state with high accuracy, requiring fewer sampling times than classical statistical sampling. They are primarily used for efficiently calculating mathematical expectation or probability values. Within this algorithmic framework, combining the previously constructed quantum payoff operator with a quantum state encoding the price distribution allows the construction of a larger quantum circuit. Through iterative execution of this circuit and quantum measurement, the mathematical expectation of the option payoff function can be estimated.

[0063] Furthermore, after obtaining the expected return of the electricity option using the quantum amplitude estimation algorithm, the target electricity option price can be determined by combining it with the no-arbitrage pricing principle from classical finance. Here, the target electricity option price refers to the theoretically fair option contract value calculated based on the model, which can be used for quotation reference, risk management, or regulatory clearing in electricity market transactions.

[0064] For example, when applying a quantum amplitude estimation algorithm to estimate the expected return, the following steps (a) to (b) may be included: (a) Using the quantum payment operator, mark the computational ground state in the quantum state that satisfies the payment condition; (b) Based on the quantum amplitude estimation algorithm, the labeled quantum state is processed to estimate the mathematical expectation of the power option payoff function under the probability distribution, and the estimation result is used as the expected payoff of the power option.

[0065] Specifically, the conditional tagging circuit in the quantum payment operator sets the state of an auxiliary qubit (often called the target qubit) to be associated with whether the price component satisfies the payment condition. For example, for the computational ground state that satisfies the payment condition (in-the-price), the auxiliary qubit is placed in quantum state |1. For the ground state that does not meet the condition (external), it is placed in |0. .

[0066] Furthermore, a quantum subroutine centered on a labeling operation can be constructed using a quantum amplitude estimation algorithm, and then embedded into the framework of quantum phase estimation. The algorithm, through a series of controlled quantum subroutine calls and inverse quantum Fourier transform operations, positions the target auxiliary qubit at |1|. The probability information of the state (which represents an unnormalized estimate of the expected return) is encoded into the phase of another register qubit; finally, by measuring the phase register and classically decoding it, the value of the expected return can be estimated.

[0067] In some possible embodiments, in order to convert future returns into present value, a preset risk-free interest rate and option expiration time can be obtained. Then, the expected return of the power option estimated by the quantum algorithm is discounted according to the continuous compound interest discount formula in finance, based on the risk-free interest rate and option expiration time. The discount calculation refers to the process of multiplying the future cash flow by a discount factor less than 1 to reflect the time value of money, thereby obtaining the target power option price.

[0068] In some other embodiments, after obtaining the target power option price, the price can be subjected to a posteriori statistical analysis, such as using historical error data for calibration or calculating confidence intervals to correct random errors and systematic biases that may occur during quantum sampling, thereby improving the robustness of the price. Subsequently, the final pricing result is pushed to the power trading platform or risk management system in real time through a standardized application programming interface, completing the closed loop from pricing calculation to actual business application.

[0069] The method proposed in this disclosure can be implemented on a variety of quantum computing hardware platforms, including superconducting quantum computers, optical quantum computing platforms, and hybrid cloud computing environments that combine quantum and classical resources, exhibiting good adaptability and scalability.

[0070] The quantum computing-based electricity option pricing method, apparatus, medium, and device provided in this disclosure introduce quantum computing technology, combining a stochastic process model of new energy electricity prices with quantum state encoding and quantum payoff operators. Furthermore, it utilizes a quantum amplitude estimation algorithm to estimate expected returns in parallel, reducing computational complexity from that of classical Monte Carlo methods. Reduced to This improves the calculation efficiency and accuracy of electricity option pricing, enabling rapid and accurate estimation of expected returns for electricity options.

[0071] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0072] Based on the same inventive concept, this disclosure also provides a quantum computing-based power option pricing device corresponding to the quantum computing-based power option pricing method. Since the principle of the device in this disclosure is similar to the quantum computing-based power option pricing method described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0073] Reference Figure 4 The diagram shown is a schematic of a quantum computing-based electricity option pricing device 400 provided in an embodiment of this disclosure. The device includes: The data acquisition module 401 is used to acquire new energy power price data; wherein, the new energy power price data includes historical power price data and at least one new energy prediction factor data; The model building module 402 is used to build a stochastic process model of new energy electricity prices based on the new energy electricity price data, and to determine the probability distribution of electricity prices at the option expiration time based on the stochastic process model. The quantum conversion module 403 is used to determine the quantum state representing the probability distribution based on the probability distribution of the electricity price; and to convert the preset electricity option payoff function into a quantum payoff operator; The price determination module 404 is used to estimate the expected payoff of the power option by applying the quantum payoff operator to the quantum state based on the quantum amplitude estimation algorithm; and to determine the target power option price based on the expected payoff of the power option.

[0074] In some possible embodiments, the model building module 402 is specifically used for: Based on the historical electricity price data, the electricity characteristics of new energy electricity prices are modeled to obtain an initial stochastic process model; wherein, the parameters of the initial stochastic process model include drift rate parameter, volatility parameter, and jump strength parameter; The new energy predictor data is input into a pre-trained prediction model to determine the correction values ​​for the drift rate parameter, volatility parameter, and jump intensity parameter; wherein, the new energy predictor data includes at least one of the following data: wind speed data, solar intensity data, and on-grid electricity price prediction data; Based on the correction value, the parameters of the initial stochastic process model are updated to obtain the stochastic process model.

[0075] In some possible embodiments, the model building module 402 is specifically used for: Based on the parameters of the stochastic process model, a quantum circuit for simulating the stochastic evolution of electricity prices is constructed. By running the quantum circuit and measuring the output quantum state of the quantum circuit, the probability distribution of the electricity price at the option's expiration time can be obtained.

[0076] In some possible embodiments, the quantum conversion module 403 is specifically used for: The number of qubits used for encoding is determined based on the number of different price states in the probability distribution. An amplitude encoding method is used to encode the probability amplitude corresponding to each price state in the probability distribution into the computational ground state amplitude represented by the qubit, thereby obtaining a quantum state; wherein, the quantum state is represented as a superposition of multiple computational ground states.

[0077] In some possible embodiments, the quantum conversion module 403 is specifically used for: The payment conditions and computational logic defined by the preset power option payment function are mapped to a sequence of quantum gate operations that perform conditional labeling and phase rotation on quantum states, forming the quantum payment operator.

[0078] In some possible embodiments, the price determination module 404 is specifically used for: The quantum payment operator is used to mark the computational ground state in the quantum state that satisfies the payment condition; Based on the quantum amplitude estimation algorithm, the labeled quantum state is processed to estimate the mathematical expectation of the power option payoff function under the probability distribution, and the estimation result is used as the expected payoff of the power option.

[0079] In some possible embodiments, the price determination module 404 is specifically used for: Obtain the preset risk-free interest rate and option expiration time; The expected return of the power option is discounted based on the risk-free interest rate and the option's expiration time to obtain the target power option price.

[0080] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 5The diagram shows the structure of a computer device 500 provided in this embodiment of the present disclosure, including a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions and includes a main memory 5021 and an external memory 5022. The main memory 5021, also called internal memory, is used to temporarily store computational data in the processor 501, as well as data exchanged with external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the main memory 5021.

[0081] In this embodiment, the memory 502 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. That is, when the computer device 500 is running, the processor 501 communicates with the memory 502 through the bus 503, so that the processor 501 executes the application code stored in the memory 502, and then executes the method described in any of the foregoing embodiments.

[0082] The memory 502 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0083] Processor 501 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0084] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 500. In other embodiments of this application, the computer device 500 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0085] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the quantum computing-based electricity option pricing method described in the above-described method embodiments. The storage medium can be either volatile or non-volatile computer-readable storage.

[0086] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the quantum computing-based electricity option pricing method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0087] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0091] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0092] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A quantum computing-based electricity option pricing method, characterized in that, include: Obtain new energy power price data; wherein, the new energy power price data includes historical power price data and at least one new energy prediction factor data; Based on the new energy power price data, a stochastic process model of new energy power price is constructed, and the probability distribution of power price at option expiration time is determined based on the stochastic process model. Based on the probability distribution of the electricity price, a quantum state representing the probability distribution is determined; and a preset electricity option payoff function is transformed into a quantum payoff operator. Based on the quantum amplitude estimation algorithm, the quantum payoff operator is applied to the quantum state to estimate the expected payoff of the power option; and based on the expected payoff of the power option, the target power option price is determined.

2. The method according to claim 1, characterized in that, The construction of the stochastic process model for new energy electricity prices includes: Based on the historical electricity price data, the electricity characteristics of new energy electricity prices are modeled to obtain an initial stochastic process model; wherein, the parameters of the initial stochastic process model include drift rate parameter, volatility parameter, and jump strength parameter; The new energy predictor data is input into a pre-trained prediction model to determine the correction values ​​for the drift rate parameter, volatility parameter, and jump intensity parameter; wherein, the new energy predictor data includes at least one of the following data: wind speed data, solar intensity data, and on-grid electricity price prediction data; Based on the correction value, the parameters of the initial stochastic process model are updated to obtain the stochastic process model.

3. The method according to claim 2, characterized in that, The determination of the probability distribution of electricity prices at option expiration based on the stochastic process model includes: Based on the parameters of the stochastic process model, a quantum circuit for simulating the stochastic evolution of electricity prices is constructed. By running the quantum circuit and measuring the output quantum state of the quantum circuit, the probability distribution of the electricity price at the option's expiration time can be obtained.

4. The method according to claim 3, characterized in that, Determining the quantum state representing the probability distribution based on the electricity price includes: The number of qubits used for encoding is determined based on the number of different price states in the probability distribution. An amplitude encoding method is used to encode the probability amplitude corresponding to each price state in the probability distribution into the computational ground state amplitude represented by the qubit, thereby obtaining a quantum state; wherein, the quantum state is represented as a superposition of multiple computational ground states.

5. The method according to claim 4, characterized in that, The process of converting a preset electricity option payment function into a quantum payment operator includes: The payment conditions and computational logic defined by the preset power option payment function are mapped to a sequence of quantum gate operations that perform conditional labeling and phase rotation on quantum states, forming the quantum payment operator.

6. The method according to claim 5, characterized in that, The quantum amplitude estimation algorithm, which applies the quantum payoff operator to the quantum state to estimate the expected payoff of the electricity option, includes: The quantum payment operator is used to mark the computational ground state in the quantum state that satisfies the payment condition; Based on the quantum amplitude estimation algorithm, the labeled quantum state is processed to estimate the mathematical expectation of the power option payoff function under the probability distribution, and the estimation result is used as the expected payoff of the power option.

7. The method according to claim 6, characterized in that, Determining the target power option price based on the expected payoff of the power option includes: Obtain the preset risk-free interest rate and option expiration time; The expected return of the power option is discounted based on the risk-free interest rate and the option's expiration time to obtain the target power option price.

8. A quantum computing-based electricity option pricing device, characterized in that, include: The data acquisition module is used to acquire new energy power price data; wherein, the new energy power price data includes historical power price data and at least one new energy prediction factor data; The model building module is used to construct a stochastic process model of new energy electricity prices based on the new energy electricity price data, and to determine the probability distribution of electricity prices at the option expiration time based on the stochastic process model. The quantum conversion module is used to determine the quantum state representing the probability distribution of the electricity price based on the probability distribution of the electricity price; and to convert the preset electricity option payoff function into a quantum payoff operator; The price determination module is used to estimate the expected payoff of the power option by applying the quantum payoff operator to the quantum state based on a quantum amplitude estimation algorithm; and to determine the target power option price based on the expected payoff of the power option.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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