Comprehensive energy system energy-saving strategy optimization method, system and equipment based on body perception

Abstract

The invention provides an integrated energy system energy-saving strategy optimization method, system and device based on body perception, and relates to the technical field of integrated energy energy-saving renovation, and the method comprises the following steps: S1, constructing a resource demand model of an integrated energy system; s2, optimizing the operation and maintenance resource demand; s3, realizing overall energy-saving benefit maximization through mutual coupling and coordinated optimization among multiple types of energy sources; s4, dynamically adjusting the energy saving amount; s5, constructing a model of user benefits and comprehensive energy company benefits in the tth year; s6, carrying out feasibility evaluation; and S7, optimizing energy planning and equipment energy-saving transformation. A management mode based on an electricity, gas and heat comprehensive energy system is provided, through coupling and optimization coordination among multiple energy sources, the energy utilization efficiency is improved, and the overall energy-saving effect maximization under multi-energy-source coordination is achieved. The overall energy efficiency of the system is effectively improved, and the energy structure is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive energy conservation transformation, and particularly relates to an optimization method, system and device for energy conservation strategies of a comprehensive energy system based on embodied perception. Background Art

[0002] With the increasingly severe global energy shortage and environmental protection issues, improving energy utilization efficiency and achieving energy conservation and emission reduction have become the focus of attention of various countries. As a market-oriented energy conservation service model, contract energy management has been widely applied globally. The contract energy management model provides a series of comprehensive services such as energy audits, energy conservation transformations, and effect monitoring for customers without increasing their financial pressure by introducing professional energy conservation service companies, thereby achieving the goals of reducing energy consumption and emissions.

[0003] Traditional energy management systems usually focus on a single type of energy, such as electricity or heat, lacking the ability to comprehensively manage multiple energy forms and being difficult to cope with complex energy demand scenarios. In addition, traditional energy conservation management means mostly rely on empirical judgments and simple control strategies, making it difficult to adapt to the increasingly complex energy usage demands in modern buildings, industries, etc. With the gradual popularization of renewable energy, the promotion of multi-energy complementary systems, and the development of smart grids, higher requirements are put forward for energy management systems - not only need to manage energy in a refined manner, but also need to be able to achieve optimized allocation and scheduling of different energy forms.

[0004] In this context, a comprehensive energy conservation and risk control system based on stochastic protocol services has emerged. This system combines modern information technology, intelligent control technology and the contract energy management model, and realizes the comprehensive management and optimized utilization of multiple energy forms (such as electricity, natural gas, solar energy, wind energy, etc.) through real-time monitoring and data analysis. This can not only improve the energy utilization efficiency, but also promote the implementation and development of energy conservation projects through the economic incentive mechanism of the contract energy management model.

[0005] However, there are still some challenges in the application of contract energy management at present: First, it is difficult to coordinate and optimize different types of energy. Especially when facing diverse energy demands, how to formulate reasonable energy conservation strategies remains a difficult problem; Second, the evaluation and tracking of energy conservation effects require more accurate and real-time data support, while traditional systems are difficult to meet this requirement. Therefore, designing a set of efficient, flexible and accurate comprehensive energy conservation system has become an important direction of current research and practice. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, and device for optimizing energy-saving strategies for integrated energy systems based on embodied perception. This system proposes a management model for integrated energy systems based on electricity, gas, and heat. By coupling and optimizing the coordination of multiple energy sources, it improves energy utilization efficiency and maximizes the overall energy savings achieved through multi-energy synergy. This effectively enhances the overall energy efficiency of the system and optimizes the energy structure.

[0007] Specifically, in a first aspect, the present invention provides a method for optimizing energy-saving strategies of an integrated energy system based on embodied perception, which comprises the following steps:

[0008] S1. Build a resource demand model for the integrated energy system based on energy planning and equipment energy-saving transformation cycles:

[0009]

[0010] Among them, I t The total resource requirements for energy planning and equipment energy-saving transformation, I C,t To invest resources in advance, I O,t To meet the operation and maintenance resource requirements, I OE,t is the operation and maintenance resource requirement during the agreement period, I OI,t The operation and maintenance resource requirements from the termination of the agreement to the end of the equipment validity period, where n is the agreement termination time and N is the project validity period end time;

[0011] S2. Operation and maintenance resource requirements I O,t As a random quantity, geometric Brownian motion is used to describe its random process to optimize the operation and maintenance resource requirements;

[0012] S3. Maximize overall energy-saving benefits through mutual coupling and coordinated optimization among multiple types of energy;

[0013] S4. Construct a geometric Brownian motion stochastic model of energy prices in an integrated energy system to dynamically adjust energy savings;

[0014] S5. Construct the user benefit R in year t I,t and integrated energy company benefits R E,t The model calculates the user benefits and the integrated energy company benefits in year t respectively:

[0015] R I,t =αG t +max{0,β(R t -G t )}

[0016] R E,t =R t -αG t -max{0,β(R t -G t)}

[0017] Among them, G t is the future annual energy-saving benefit, α and β are the user guarantee benefit ratio coefficients respectively, and R t is the actual energy-saving benefit in the t-th year;

[0018] S6. Conduct a feasibility assessment on energy planning and equipment energy-saving transformation:

[0019]

[0020] Among them, the subscript s represents the s-th scenario, with a total of M scenarios. NPV I,S and NPV E,S are the net present values of the user and the integrated energy company over the entire project life cycle respectively, and r I and r E are the benchmark benefit rates of the user and the integrated energy company respectively; if NPV≥0, the plan is feasible; if NPV<0, the plan is infeasible;

[0021] S7. Based on the feasibility evaluation results, construct an optimization model with the maximization of the user's net present value as the optimization goal to optimize energy planning and equipment energy-saving transformation:

[0022]

[0023] Among them, X (t,s) is a binary variable, 1 indicates that the protocol is executed, and 0 indicates that the protocol is terminated.

[0024] Preferably, in step S2, the operation and maintenance resource requirement I O,t is regarded as a random variable, and its random process is described by geometric Brownian motion, specifically as follows:

[0025]

[0026] Among them, the subscript s represents the s-th scenario, with a total of M scenarios, ω is the change trend of the operation and maintenance resource requirement over time; H0 is the quantitative value of the geometric Brownian motion random process, and the function exp(x) is the natural exponential function, δ I is the volatility of the operation and maintenance resource requirement per year, and ε I,s represents the random error, and ε I,s conforms to the standard normal distribution.

[0027] Preferably, in step S3, the maximization of the overall energy-saving benefit is achieved through the mutual coupling and coordinated optimization among multiple types of energy, which is specifically expressed as:

[0028]

[0029] In the formula, R t,sIndicates the overall energy-saving benefit of the integrated energy project in the s-th scenario in the t-th year; p E,t 、p G,t 、p H,t respectively represent the energy prices of electricity, natural gas, and heat in the t-th year; Q E,t 、Q G,t 、Q H,t respectively represent the energy savings of electricity, natural gas, and heat in the t-th year.

[0030] Preferably, the geometric Brownian motion stochastic model of the integrated energy system energy price constructed in step S4 is specifically:

[0031]

[0032] wherein, P0 is the initial value of the energy price, including the initial values p E,0 、p G,0 、p H,0 of the electricity, natural gas, and heating prices; μ P is the drift rate of the energy price change, including the drift rates μ PE 、μ P,G 、μ P,H of the electricity, natural gas, and heating price changes; δ P 、δ Q are respectively the standard deviations of the energy price and energy savings changes; ε P,S and ε Q,S respectively represent the random errors of the energy price and energy savings; K0 represents the energy savings generated by the unit investment resource demand, I C K0 is the initial energy savings, and the random change trend of the energy savings after the agreement comes into effect follows a geometric Brownian motion distribution; P t,s represents the prices of electricity, gas, and heat in the s-th scenario in the t-th year, Q t,s represents the energy savings of the three types of energy in the s-th scenario in the t-th year; f t is the equipment loss situation in the t-th year, the degree of equipment loss is proportional to time, and the energy savings is inversely proportional to time.

[0033] Preferably, the value of β in step S5 is greater than the value of α.

[0034] Preferably, ε P,S and ε Q,S in step S4 conform to the standard normal distribution.

[0035] Preferably, in step S2, the operation and maintenance resource demand is optimized by reducing the upfront investment resources.

[0036] Preferably, in step S2, if ω > 1, the operation and maintenance resource demand increases year by year; if ω < 1, the operation and maintenance resource demand decreases year by year.

[0037] In a second aspect, the present invention provides an integrated energy conservation and risk control strategy optimization system, which includes a resource demand model construction unit, an operation and maintenance resource demand random unit, a coordination unit, an energy price random unit, a benefit model construction unit, a feasibility evaluation unit, and a project optimization unit;

[0038] The resource demand model construction unit is used to construct a resource demand model for integrated energy;

[0039] The operation and maintenance resource demand random unit is used to take the operation and maintenance resource demand I O,t as a random variable and describe its random process using geometric Brownian motion;

[0040] The coordination unit is used to maximize the overall energy conservation benefit through the mutual coupling and coordinated optimization among multiple types of energy;

[0041] The energy price random unit is used to give the random process of geometric Brownian motion of the energy price;

[0042] The benefit model construction unit is used to construct the user benefit R I,t in the t-th year and the integrated energy company benefit R E,t model;

[0043] The feasibility evaluation unit is used to evaluate the feasibility of the project;

[0044] The project optimization unit is used to construct an optimization equation based on the project feasibility evaluation result and with the maximization of the user's net present value as the optimization goal for optimization.

[0045] In a third aspect of the present invention, there is provided an integrated energy conservation and risk control strategy optimization device, which includes a computer device and a storage medium. The storage medium stores the above-mentioned integrated energy conservation and risk control strategy optimization system internally and can execute the strategy optimization method.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] (1) By introducing the integrated energy conservation strategy optimization method, the user can transfer the high upfront investment resource demand and part of the operation risk to the integrated energy company, so that the user can enjoy the energy conservation benefit of the integrated energy system with lower risk and less initial investment, significantly reducing the threshold for the user to participate in energy conservation transformation.

[0048] (2) The present invention proposes an energy conservation strategy optimization method based on the integrated electricity, gas, and heat energy system. Through the coupling and optimized coordination among multiple energies, the energy utilization efficiency is improved, the overall energy conservation effect under the coordination of multiple energies is maximized, the overall energy efficiency of the system is effectively enhanced, and the energy structure is optimized.

[0049] (3) The energy-saving effect sharing mechanism in the optimized mode of the present invention can encourage both parties to jointly focus on the long-term benefits of energy-saving projects. By accurately evaluating the energy-saving potential, the integrated energy company provides guaranteed energy-saving effects for users and establishes a reasonable effect distribution model, which helps to motivate the integrated energy company to improve the accuracy of energy-saving evaluation and optimize the energy-saving management strategy. Description of the Drawings

[0050] Figure 1 It is a schematic flow chart of the energy-saving strategy optimization method for an integrated energy system based on embodied perception of the present invention;

[0051] Figure 2 It is a block diagram of the energy-saving strategy optimization for an integrated energy system based on embodied perception of the present invention;

[0052] Figure 3 It is a system flow chart in an embodiment of the present invention;

[0053] Figure 4 It is an energy-saving effect diagram in an embodiment of the present invention;

[0054] Figure 5 It is a relationship diagram between the energy-saving effect guarantee value and the net present value of the integrated energy company in an embodiment of the present invention;

[0055] Figure 6 It is a relationship diagram between the over-energy-saving effect distribution ratio and the net present value of the integrated energy company in an embodiment of the present invention. Detailed Embodiment

[0056] Hereinafter, the embodiments of the present invention will be described with reference to the drawings.

[0057] Specifically, in the first aspect, the present invention provides an energy-saving strategy optimization method for an integrated energy system based on embodied perception, as Figure 1 shown, which includes the following steps:

[0058] S1. Based on the energy plan and the equipment energy-saving transformation cycle, construct a resource demand model for the integrated energy system:

[0059]

[0060] Wherein, I t is the total resource demand for energy planning and equipment energy-saving transformation, I C,t is the upfront investment resource, I O,t is the operation and maintenance resource demand, I OE,t is the operation and maintenance resource demand during the agreement period, I OI,t is the operation and maintenance resource demand from the end of the agreement to the expiration of the equipment validity period, n is the end time of the agreement, and N is the expiration time of the project validity period.

[0061] In specific energy-saving renovations, the integrated energy company will provide users with energy-saving renovations such as energy planning and equipment transformation. The energy-saving renovation has a certain life cycle, generally 20 - 30 years. The validity period of the entire integrated energy service project can be divided into three stages: before the agreement execution, during the agreement period, and from the agreement termination to the project validity period. As can be seen from the above formula, the resource requirements of the entire project include two parts: the upfront investment resource I C,t and the operation and maintenance resource requirement I O,t , where the upfront investment resource is paid by the integrated energy company before the start of the agreement (t = 0); the operation and maintenance resource requirement I OE,t during the agreement period (t = 1, 2,..., n) is paid by the integrated energy company; the operation and maintenance resource requirement I OI,t from the termination of the agreement to the end of the project validity period (t = n + 1,..., N) is borne by the user.

[0062] S2. Taking the operation and maintenance resource requirement IO,t as a random variable, using geometric Brownian motion to describe its random process, and optimizing the operation and maintenance resource requirement. Specifically:

[0063]

[0064] where the subscript s represents the s scenario, with a total of M scenarios, ω is the change trend of the operation and maintenance resource requirement over time. If ω > 1, the operation and maintenance resource requirement increases year by year; if ω < 1, the operation and maintenance resource requirement decreases year by year. H0 is the quantitative value of the geometric Brownian motion random process, the function exp(x) is the natural exponential function, δ I is the volatility of the annual operation and maintenance resource requirement, ε I,s represents the random error, and ε I,s follows the standard normal distribution.

[0065] In the integrated energy-saving project, the operation and maintenance resource requirement will be affected by various uncertain factors, such as fluctuations in energy prices, fluctuations in the output of multiple types of energy, and the aging of energy equipment, etc. Therefore, taking the operation and maintenance resource requirement I O as a random variable, using geometric Brownian motion (geometric Brownian motion, GBM) to describe its random process. As can be seen from the above, the operation and maintenance resource requirement is determined by the upfront investment I c and the quantitative value H0 of the geometric Brownian motion random process. Therefore, the larger the upfront investment, the higher the annual operation and maintenance resource requirement of this energy-saving project. In specific applications, the operation and maintenance resource requirement can be optimized by reducing the upfront investment resource.

[0066] S3. Achieving the maximization of the overall energy-saving benefit through the mutual coupling and coordinated optimization among multiple types of energy:

[0067]

[0068] In the formula, R t,s represents the overall energy-saving benefit of the integrated energy project in the t-th year under the s-th scenario; p E,t , p G,t , p H,t represent the energy prices of electricity, natural gas, and heat in the t-th year respectively; Q E,t , Q G,t , Q H,t represent the energy-saving amounts of electricity, natural gas, and heat in the t-th year respectively. It can be seen from the above formula that the energy-saving benefit of the entire project is the product of the energy prices and energy-saving amounts of multiple types of energy such as electricity, gas, and heat.

[0069] S4. Construct a geometric Brownian motion stochastic model of the integrated energy system energy price to dynamically adjust the energy-saving amount. The specific geometric Brownian motion stochastic model of the integrated energy system energy price constructed is as follows:

[0070]

[0071] where P0 is the initial value of the energy price, including the initial values p E,0 , p G,0 , p H,0 of the electricity, natural gas, and heating prices; μ P is the drift rate of the energy price change, including the drift rates μ PE , μ P,G , μ P,H of the electricity, natural gas, and heating price changes; δ P , δ Q are the standard deviations of the energy price and energy-saving amount changes respectively; ε P,S and ε Q,S represent the random errors of the energy price and energy-saving amount respectively, and ε P,S and ε Q,S conform to the standard normal distribution. K0 represents the energy-saving amount generated by the unit investment resource demand, I C K0 is the initial energy-saving amount, and the random change trend of the energy-saving amount after the agreement comes into effect follows the geometric Brownian motion distribution; P t,s represents the prices of electricity, gas, and heat in the t-th year under the s-th scenario, and Q t,s represents the energy-saving amounts of the three types of energy in the t-th year under the s-th scenario; f t is the equipment loss situation in the t-th year, and the degree of equipment loss is proportional to time, while the energy-saving amount is inversely proportional to time.

[0072] In practical applications, there are uncertainties in the energy prices and energy-saving amounts in the future years after the project starts. Through the constructed geometric Brownian motion stochastic model of the integrated energy system energy price, the energy-saving amounts in different scenarios can be dynamically adjusted.

[0073] S5. Construct the user benefit R in the t-th year I,t and the integrated energy company benefit R E,t models to calculate the user benefit and the integrated energy company benefit in the t-th year respectively:

[0074] R I,t = αG t + max{0, β(R t - G t )}

[0075] R E,t = R t - αG t - max{0, β(R t - G t )}

[0076] where G t is the future annual energy-saving benefit, and α and β are the user guarantee benefit ratio coefficients respectively, and the value of β is greater than the value of α. R t is the actual energy-saving benefit in the t-th year.

[0077] S6. Conduct a feasibility assessment of the energy plan and equipment energy-saving transformation:

[0078]

[0079] [[ID=4,4]]where NPV I,S and NPV E,S are the net present values of the user and the integrated energy company over the entire project life cycle respectively, and r I and r E are the benchmark benefit rates of the user and the integrated energy company respectively; if NPV ≥ 0, the plan is feasible; if NPV < 0, the plan is not feasible.

[0080] The integrated energy company's estimate of the project benefit will affect the implementation of the agreement. As can be seen from the above formula, if the actual benefit R t during the agreement period fails to reach the estimated energy-saving benefit G t , it will be difficult for the integrated energy company to recover the investment resource requirements. Therefore, in order to encourage the integrated energy company to estimate the energy-saving benefit as accurately as possible, generally β > α is set, and the user will receive more benefits when the target is exceeded. During the energy-saving transformation period, if the agreed energy-saving benefits are not achieved, the user will receive the minimum benefit guarantee; if the energy-saving benefits are exceeded, the user will share the excess energy-saving benefits according to the embodied perception technology and the stochastic price model to achieve the dynamic balance of the benefits of both parties.

[0081] S7. Based on the feasibility evaluation results, construct an optimization model with the maximization of the user's net present value as the optimization goal, and optimize the energy plan and equipment energy-saving transformation:

[0082]

[0083] Among them, X (t,s) is a binary variable, where 1 represents the execution of the protocol and 0 represents the termination of the protocol.

[0084] The protocol term optimization decision-making scheme proposed by this technology aims to balance the interests of both parties of the protocol, with the maximization of the user's net present value (NPV) as the optimization goal, while ensuring that the integrated energy company can obtain the expected investment return, that is, NPV E ≥0. Considering the random factors of energy-saving projects comprehensively, the decision-making model introduces a binary variable X(t, s) to describe the dynamic continuity of protocol execution to balance the benefits and risks of both parties. Among them, 1 represents the execution of the protocol and 0 represents the termination of the protocol, and then decides whether to extend the validity period of the contract. At the same time, to ensure the continuity of protocol execution, the agreed logical constraint conditions make the decision-making process of the protocol more stable and forward-looking in practical applications. The integrated energy company shares the energy-saving benefits with users through the energy-saving benefit sharing model; during the protocol period, the integrated energy company assumes part or all of the investment risks of the project and provides energy-saving benefit guarantees for users through embodied perception and stochastic contract mechanisms.

[0085] The method of the present invention can, by adding embodied perception technology, real-time sense the user's energy usage behavior and environmental changes, and adjust the protocol terms according to these dynamic information, including the optimal protocol period, benefit distribution scheme and risk guarantee mechanism, so as to enhance the flexibility of the energy-saving transformation protocol and balance the risks and benefits of both parties of the energy-saving transformation protocol. The system realizes the maximization of energy-saving benefits and the minimization of risks, and promotes the popularization and application of the integrated energy system.

[0086] In a second aspect, the present invention provides an integrated energy energy-saving and risk control strategy optimization system, as Figure 2 shown, which includes a resource demand model construction unit 1, an operation and maintenance resource demand random unit 2, a coordination unit 3, an energy price random unit 4, a benefit model construction unit 5, a feasibility evaluation unit 6 and a project optimization unit 7.

[0087] The resource demand model construction unit 1 is used to construct a resource demand model for integrated energy.

[0088] The operation and maintenance resource demand random unit 2 is used to take the operation and maintenance resource demand I O,t as a random variable and describe its random process using geometric Brownian motion.

[0089] The coordination unit 3 is used to maximize the overall energy-saving benefit through the mutual coupling and coordinated optimization among multiple types of energy.

[0090] The energy price random unit 4 is used to give the random process of geometric Brownian motion of the energy price.

[0091] The benefit model construction unit 5 is used to construct the user benefit R in the t-th year I,t and the integrated energy company benefit R E,t model.

[0092] The feasibility evaluation unit 6 is used to conduct a feasibility evaluation of the project.

[0093] The project optimization unit 7 is used to construct an optimization equation based on the project feasibility evaluation result and with the maximization of the net present value of the user as the optimization goal for optimization.

[0094] The third aspect of the present invention provides an integrated energy energy-saving and risk control strategy optimization device, which includes a computer device and a storage medium. The storage medium stores the above-mentioned integrated energy energy-saving and risk control strategy optimization system internally. Specific embodiment

[0096] In this embodiment, the multi-energy user, as the energy-saving service object of the integrated energy company, signs an integrated energy agreement for building energy-saving renovation of the energy-saving benefit sharing type with the integrated energy company. The integrated energy company provides energy-saving services for its users according to the agreement requirements, and recovers the resource requirements by sharing the energy-saving benefits with the integrated energy company.

[0097] This embodiment provides an integrated energy system energy-saving strategy optimization method based on embodied perception, which optimizes the energy-saving strategy for the building energy-saving renovation, and includes the following steps:

[0098] S1. Based on the building energy plan and the equipment energy-saving renovation cycle, construct a resource demand model of the integrated energy system:

[0099]

[0100] Among them, I t is the total resource demand for the building energy plan and equipment energy-saving renovation, I C,t is the pre-invested resource, I O,t is the operation and maintenance resource demand, I OE,t is the operation and maintenance resource demand during the agreement period, I OI,t is the operation and maintenance resource demand from the end of the agreement to the end of the equipment validity period, n is the agreement termination time, and N is the project validity period end time.

[0101] S2. Take the operation and maintenance resource demand I O,t of the building energy-saving renovation as a random variable, use geometric Brownian motion to describe its random process, and optimize the operation and maintenance resource demand. Specifically:

[0102]

[0103] S3. Maximize the overall energy-saving benefit of building energy-saving renovation by mutual coupling and coordinated optimization among multiple types of energy:

[0104]

[0105] S4. Construct a geometric Brownian motion stochastic model of the energy price of the integrated energy system for building energy-saving renovation to dynamically adjust the energy-saving amount. The constructed geometric Brownian motion stochastic model of the energy price of the integrated energy system is specifically as follows:

[0106]

[0107] S5. Construct the user benefit R I,t and the integrated energy company benefit R E,t models to calculate the user benefit and the integrated energy company benefit in the t-th year respectively:

[0108] R I,t = αG t + max{0, β(R t - G t )}

[0109] R E,t = R t - αG t - max{0, β(R t - G t )}.

[0110] S6. Conduct a feasibility assessment on the energy plan and equipment energy-saving renovation. The evaluated plan in this embodiment is feasible.

[0111] S7. Based on the feasibility evaluation result, construct an optimization model with the maximization of the user's net present value as the optimization goal to optimize the energy plan and equipment energy-saving renovation.

[0112] The specific example data of this embodiment is shown in Table 1. The optimal agreement period obtained by weighted averaging different random scenarios is 20 years. At this time, the corresponding user benefit is 1.5424 million yuan; the integrated energy company benefit is 1.0949 million yuan, as Figure 4 shown. It can be seen that the energy-saving benefit of the project shows an upward trend after the start of the agreement. However, due to problems such as equipment loss, the energy-saving amount shows a decreasing trend year by year, so the energy-saving benefit gradually decreases in the later stage.

[0113] Table 1 Parameter settings in the agreement decision model

[0114]

[0115] [[ID=...]]

[0116] The protocol's guaranteed energy-saving benefit G and the distribution ratio β of the excess benefit greatly affect the NPV value of the integrated energy company. The energy-saving fluctuation coefficient δ Q represents the prediction error of the energy-saving amount within the future protocol period of the integrated energy company. The larger the fluctuation coefficient, the greater the prediction error of the future energy-saving amount. As can be seen from Figure 5 when the protocol's guaranteed benefit is less than 1 million yuan under different energy-saving fluctuation coefficients, the NPV value of the integrated energy company remains positive; when it is higher than 1 million yuan, the NPV value becomes negative. Therefore, considering the risk of large prediction errors caused by the random fluctuation of future energy-saving errors, the protocol should set the guaranteed energy-saving benefit G within 1 million yuan. Figure 6 This reflects the relationship between the distribution ratio β of the excess benefit and the NPV value of the integrated energy company. The larger β is, the larger the proportion of the future excess energy-saving benefit that needs to be distributed to users, and the less benefit the integrated energy company can obtain. Under the influence of different δ Q when the distribution ratio β of the excess benefit of the integrated energy company is less than 0.5, the NPV value of the integrated energy company remains positive, indicating that the integrated energy company can recover the resource demand and obtain excess benefits; when β > 0.5, the NPV value becomes negative, and the project is not feasible. Therefore, the protocol should set the value of β within 0.5.

[0117] In summary, in the integrated energy-saving project of this calculation example, the agreement signed between the integrated energy company and the user should set the protocol-guaranteed energy-saving benefit G to 1 million yuan and the distribution ratio β of the excess benefit to 0.5. Considering the influence of various future uncertain factors, based on the stochastic models of three energy prices and energy-saving amounts, the optimal protocol period for balancing the interests of both parties to the agreement is obtained as 20 years. At this time, the user can make a profit of 1.5424 million yuan, and the integrated energy company can recover the previous investment resource demand and the operation and maintenance resource demand during the protocol period, and obtain an energy-saving benefit of 1.0949 million yuan.

[0118] In summary, in the embodiments of the present invention, by introducing the integrated energy-saving strategy optimization method and the embodied perception technology, the user can transfer the high upfront investment resource demand and part of the operation risk to the integrated energy company, so that the user can enjoy the energy-saving benefits of the integrated energy system with lower risk and less initial investment, significantly reducing the threshold for the user to participate in energy-saving transformation. At the same time, during the protocol period, the integrated energy company can regularly detect and randomly verify the energy-saving benefits, and through the embodied perception technology and the data-driven analysis model, it can real-time perceive the user's needs and behavior changes, and dynamically adjust the energy-saving transformation.

[0119] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the spirit of the present invention's design, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An optimization method for energy-saving strategies of an integrated energy system based on embodied perception, characterized in that: It includes the following steps: S1. Based on the energy plan and the equipment energy-saving transformation cycle, construct a resource demand model for the integrated energy system: Among them, I t is the total resource demand for energy planning and equipment energy-saving transformation, I C,t is the initially invested resource, I O,t is the operation and maintenance resource demand, I OE,t is the operation and maintenance resource demand during the agreement period, I OI,t is the operation and maintenance resource demand from the termination of the agreement to the expiration of the equipment validity period. n is the agreement termination time, and N is the project expiration time; S2. Take the operation and maintenance resource requirement I O,t as a random variable, describe its random process using geometric Brownian motion, and optimize the operation and maintenance resource requirement; S3. Achieve the maximization of the overall energy-saving benefit through the mutual coupling and coordinated optimization among multiple types of energy; S4. Construct a geometric Brownian motion stochastic model of the integrated energy system energy price to dynamically adjust the energy-saving amount; S5. Construct the user benefit \(R\) in the \(t\)-th year I,t and the integrated energy company benefit \(R\) E,t Calculate the user benefit and the integrated energy company benefit in the \(t\)-th year respectively using the models: R I,t = αG t + max{0, β(R t - G t )} R E,t = R t - αG t - max{0, β(R t - G t )} Among them, G t is the future annual energy-saving benefit, α and β are the user guarantee benefit ratio coefficients respectively, and R t is the actual energy-saving benefit in the t-th year; S6. Conduct a feasibility assessment on the energy plan and equipment energy-saving transformation: Among them, the subscript s represents the s-th scenario, with a total of M scenarios, and NPV I,S and NPV E,S are the net present values of the user and the integrated energy company over the entire project life cycle, respectively. r I and r E are the benchmark benefit rates of the user and the integrated energy company, respectively. If NPV ≥ 0, the plan is feasible; if NPV < 0, the plan is infeasible. S7. Based on the feasibility evaluation result, construct an optimization model with the maximization of the user's net present value as the optimization goal to optimize the energy plan and equipment energy-saving transformation: Among them, X (t,s) is a binary variable, where 1 indicates protocol execution and 0 indicates protocol termination. s represents the s-th scenario, and there are a total of M * M scenarios.

2. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 1, characterized in that: Step S2 takes the operation and maintenance resource requirement I O,t as a random variable and describes its random process using geometric Brownian motion, specifically as follows: where ω is the change trend of operation and maintenance resource requirements over time; H0 is the quantitative value of the geometric Brownian motion random process, the function exp(x) is the natural exponential function, and δ I is the volatility of the annual operation and maintenance resource requirements, and ε I,s represents the random error, and ε I,s follows the standard normal distribution.

3. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 2, wherein: In step S3, the achievement of the maximization of the overall energy-saving benefit through the mutual coupling and coordinated optimization among multiple types of energy is specifically expressed as: where R t,s represents the overall energy-saving benefit of the integrated energy project in the s-th scenario and the t-th year; p E,t , p G,t , p H,t respectively represent the energy prices of electric energy, natural gas, and heat energy in the t-th year; Q E,t , Q G,t , Q H,t respectively represent the energy-saving amounts of electric energy, natural gas, and heat energy in the t-th year.

4. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 3, wherein: The geometric Brownian motion stochastic model of the integrated energy system energy price constructed in step S4 is specifically: Among them, P0 is the initial value of energy prices, including the initial values p of electricity, natural gas, and heating prices E,0 , p G,0 , p H,0 ; μ P is the drift rate of energy price changes, including the drift rates μ PE , μ P,G , μ P,H ; δ P , δ Q are the standard deviations of energy price and energy savings changes respectively; ε P,S and ε Q,S represent the random errors of energy price and energy savings respectively; K0 represents the energy savings generated by unit investment resource demand, I C K0 is the initial energy savings, and the random change trend of energy savings after the agreement comes into effect follows a geometric Brownian motion distribution; P t,s represents the prices of electricity, gas, and heat in the t-th year under the s-th scenario, Q t,s represents the energy savings of the three types of energy in the t-th year under the s-th scenario; f t is the equipment loss situation in the t-th year. The degree of equipment loss is proportional to time, and the energy savings is inversely proportional to time.

5. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 3, characterized in that: In step S5, the value of β is greater than the value of α.

6. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 3, wherein: In step S4, ε P,S and ε Q,S conform to the standard normal distribution.

7. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 3, wherein: In step S2, optimize the operation and maintenance resource demand by reducing the upfront input resources.

8. The energy-saving strategy optimization method for an integrated energy system based on embodied perception according to claim 3, characterized in that: In step S2, if ω > 1, the operation and maintenance resource demand increases year by year; if ω < 1, the operation and maintenance resource demand decreases year by year.

9. An energy-saving strategy optimization system for the energy-saving strategy optimization method of the embodied perception-based integrated energy system according to any one of claims 1-8, characterized in that: It includes a resource demand model construction unit, an operation and maintenance resource demand random unit, a coordination unit, an energy price random unit, a benefit model construction unit, a feasibility assessment unit, and a project optimization unit; The resource demand model construction unit is used to construct a resource demand model for the integrated energy; The operation and maintenance resource demand random unit is used to take the operation and maintenance resource demand I O,t as a random variable and describe its random process using geometric Brownian motion; The coordination unit is used to achieve the maximization of the overall energy-saving benefit through the mutual coupling and coordinated optimization among multiple types of energy; The energy price random unit is used to give a geometric Brownian motion stochastic process of the energy price; The benefit model construction unit is used to construct the user benefit R in the t-th year I,t and the integrated energy company benefit R E,t model; The feasibility assessment unit is used to conduct a feasibility assessment on the project; The project optimization unit is used to construct an optimization equation for optimization based on the project feasibility evaluation result with the maximization of the user's net present value as the optimization goal.

10. An integrated energy-saving strategy optimization device, characterized in that: It includes a computer device and a storage medium, and the storage medium stores internally the energy-saving strategy optimization system as claimed in claim 9 and is capable of executing the energy-saving strategy optimization method.

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