A method and device for optimizing the safety of a two-level energy system in an urban park

Abstract

The present invention discloses a method and device for optimizing the safety of a two-level energy system in a city park. The device includes a system perception module, an energy security analysis module, an economic operation module, and an efficiency improvement module; the system perception module is used for real-time state perception and monitoring of the urban integrated energy system UIES and the park integrated energy system CIES; the energy security analysis module is used for formulating the best energy safety operation strategy for UIES with UIES as the main body; the economic operation module is used for formulating specific economic operation goals and corresponding system constraints with CIES as the main body, treating a single CIES as an independent operating subject; the efficiency improvement module is used for realizing the coordinated optimization of multiple parks, multiple subjects and the city. The scheme takes into account the coordinated optimization of the two-layer energy systems of the city and the park. After experiments, it leads to a 7.4% reduction in the safety cost of the urban UIES, and a 14.3% reduction in the operating cost of the park CIES.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive energy system safety improvement, and in particular to a method and device for safety optimization of a two-level energy system in an urban park. Background Art

[0002] The operation optimization method of Urban Integrated Energy System (UIES) is an effective tool to achieve complementary advantages among different energy sources and improve energy utilization efficiency and energy supply safety, economy, flexibility and environmental protection. The operation of the urban integrated energy system with multiple energy coupling has the operation characteristics of the traditional energy system, and there are differences in the coupling of multiple energy sources. With the influx of more heterogeneous energy and loads, how to explore the operation characteristics of multiple energy sources, integrate their operation advantages, and achieve the basic operation requirements of the energy system including safety, economy and environmental protection has become one of the key research directions of the current urban integrated energy system operation optimization technology. As an important part of the urban integrated energy system, the community integrated energy system (CIES) has been put into practical application in many projects at home and abroad.

[0003] With the development of information Internet of Things, artificial intelligence technology and the construction of multi-energy networks, the interaction and operation mechanism of distributed energy systems are gradually evolving into a flatter structure. In this context, building an energy system that can both meet the needs of energy suppliers and support distributed energy decentralization has become the mainstream trend for future energy systems to participate in operations, interactions and transactions.

[0004] In the framework of the urban integrated energy system, although it includes multiple independent park integrated energy systems and can realize a decentralized operation strategy, there are two significant challenges: 1) Each park energy system operates independently with its own economic and safety strategies, lacking a safety analysis from an overall perspective. 2) The park decision-makers have a strong subjective awareness of the park's safety and privacy, resulting in low economic benefits and lack of safety in the actual operation of the regional integrated energy system. Summary of the invention

[0005] In view of the technical problems pointed out in the background technology, the purpose of the present invention is to provide a method and device for safety optimization of a two-level energy system in an urban park.

[0006] To achieve the purpose of the present invention, the technical solution provided by the present invention is specifically as follows:

[0007] First aspect

[0008] The present application provides a two-level energy system security optimization device for an urban park, including a system perception module, an energy security analysis module, an economic operation module, and an efficiency improvement module;

[0009] The system perception module is used for real-time status perception and monitoring of the urban integrated energy system UIES and the park integrated energy system CIES;

[0010] The energy security analysis module is used to formulate the best energy security operation strategy of UIES with UIES as the main body;

[0011] The economic operation module is used to take CIES as the main body, treat a single CIES as an independent operation subject, and formulate specific economic operation goals and corresponding system constraints;

[0012] The efficiency improvement module is used to achieve coordinated optimization of multiple parks, multiple entities and cities.

[0013] Second aspect

[0014] The present application also provides a method for safely optimizing a two-level energy system in an urban park, and the method is performed using the above-mentioned device.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] (1) The scheme takes into account the coordinated optimization of the two-level energy systems of the city and the park. After testing, it leads to a 7.4% reduction in the safety cost of the city UIES and a 14.3% reduction in the operating cost of the park CIES.

[0017] (2) In addition, the efficiency improvement module speeds up the decision-making process by enhancing the computing efficiency of the energy security analysis module and the economic operation module, thereby improving the response speed and processing capacity of the entire system. After testing, it was found that under the same hardware facility deployment conditions, the computing time can be reduced by more than 45%. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a device module provided in an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of the iterative process of UIES and CIES in the test example of this application;

[0020] Figure 3 This is a schematic diagram of the heat energy trading results between UIES and CIES in the test example of this application. DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The present invention provides a method and device for safety optimization of a two-layer energy system in an urban park. The method and device propose a hybrid game two-layer operation optimization architecture for an urban park energy system that takes safety constraints into consideration, including: establishing a two-layer low-carbon economic operation target for an urban park energy system based on safety constraints; conducting operation modeling through safety constraints, power constraints, low-carbon transactions, and price ranges for the urban park system: conducting game transformation on the established operation model to obtain expected expectations that take into account economic and energy interactions, thereby achieving the generation of the best safety operation strategy for the upper-level urban system and the best economic-safety operation of each park energy system at the lower level; in addition, in response to the heavy computational burden caused by large-scale urban energy systems, the present invention proposes an ADMM algorithm with adaptive update of penalty factors. The use of the two-layer hybrid game method of the present invention can ensure that the optimization effect between multi-level and multi-agent urban-park energy systems is better than that of traditional energy optimization methods.

[0023] like Figure 1 As shown, this embodiment provides a two-level energy system security optimization device for a city park, including a system perception module, an energy security analysis module, an economic operation module, and an efficiency improvement module;

[0024] Among them, the system perception module is mainly responsible for the real-time status perception and monitoring of UIES and CIES. This module first collects key data from UIES and CIES through a variety of sensors and data acquisition units, including but not limited to energy consumption, system performance, and equipment operating status. The collected key data undergoes preliminary preprocessing, removes noise, and is standardized for subsequent analysis.

[0025] Subsequently, the system perception module removes outliers from the processed data. In order to capture any key variables that may affect the operation of the system in real time. In addition, the system perception module is also equipped with a dynamic feedback system that can fit the historical data with the data collected by the system perception module (including: the injected power of each device, the output power, and the power conversion efficiency calculated based on this), and perform reliability analysis. Based on the analysis results, the data collection frequency and accuracy are automatically adjusted to optimize the perception efficiency and response speed of the entire system. This mechanism ensures that the module can maintain optimal performance under different operating conditions and demand changes. Finally, the real-time data provided by the system perception module will be sent to the energy security analysis module and the economic operation module to support the decision-making of these two modules and ensure the safe operation of the entire system and maximize economic benefits.

[0026] The energy security analysis module is based on UIES and aims to formulate the best energy security operation strategy for UIES. The energy security analysis module is specifically used to:

[0027] (1) Establish the optimal operation objective function of the energy security analysis module, including the lowest energy operation risk and the lowest energy sales. Generate the objective function of the energy security analysis module:

[0028]

[0029] Where F is the total economic-safety benefit of the entire UIES; n is the integrated energy system of a certain park; t is the time when the system is running, with a step length of hours; T is the total time when the system is running; and They represent the electricity sales price of UIES to CIES and the large power grid at time t, and represents the price of electricity purchased by UIES from CIES and the large power grid at time t; They represent the electricity traded between UIES and CIES at time t and the electricity traded with the large power grid. Represents the operating load loss cost of the system.

[0030] (2) Based on the data output of the system perception module, determine the operational feasibility boundary constraints of UIES.

[0031] Purchase and sale price constraints: To prevent unreasonable price fluctuations and excessively high electricity prices. The electricity price is required to fluctuate within a reasonable range. The specific electricity price constraints are shown in formula (2):

[0032]

[0033] In the formula, Represents the price transaction vector of UIES, and A vector of upper and lower limits representing the UIES transaction price.

[0034] Price Fluctuation Control: To avoid unreasonable price fluctuations and price surges during the optimization process, this module imposes constraints on the maximum average purchase and sale price. This constraint aims to stabilize electricity prices and ensure that prices are fair and reasonable, thereby supporting the economic benefits and energy security of the system.

[0035]

[0036] In the formula, It is the maximum average purchase and sale price vector given by UIES to each CIES.

[0037] The economic operation module is based on CIES. In the economic operation module, a single CIES is regarded as an independent operation entity, and the operation of a single CIES needs to consider other CIES connected to it. The following content describes the impact of the operation and scheduling of CIES on its economic efficiency:

[0038] (1) Energy flow coordination: There is energy flow between CIES. This module is responsible for coordinating energy flow, ensuring efficient energy transfer between CIES, and optimizing overall energy utilization.

[0039] (2) Gas supply management: CIES needs to obtain gas supply from UIES to support internal multi-energy equipment for energy conversion. Ensuring the stability of gas supply is the key to optimizing economic operation. is the input variable.

[0040] (3) Formulate specific economic operation objectives and corresponding system constraints to support decision-making and implementation of optimization strategies to ensure the maximization of CIES’ economic benefits.

[0041] Economic objective function setting: Setting the economic objective of CIES operation aims to minimize the total operating cost during system operation. The specific objective function is as follows, which is used to guide system operation and optimize decision-making:

[0042]

[0043] Where F(C) represents the CIES economic objective function, Represents the system gas purchase cost; represents the transaction costs with UIES; represents the energy transaction cost between various parks at time t; Represents the operating cost of the system equipment; represents the demand response cost; The network fee represents the energy; Represents the cost of carbon trading.

[0044] The various costs include energy purchase costs, UIES electricity sales and purchase costs, CIES energy exchange costs, CIES internal demand response costs, and power loss costs in each link. The specific expressions correspond to (5)-(11) as shown below:

[0045]

[0046] In the formula, Represents the gas purchase price from the gas company, Represents the gas purchase volume.

[0047]

[0048] In the formula, and They are the electricity purchase and sales of the nth CIES from UIES, and Represents the electricity purchase and sale price between park n and park m.

[0049]

[0050] In the formula, and Represents the electricity sales price and electricity purchase price between park n and park m; and Represents the heat selling price and heat purchasing price between park n and park m; and Represents the electricity sales and purchase between park n and park m; and Represents the heat sales and purchase between park n and park m.

[0051]

[0052] In the formula, v represents the vector of the operating cost coefficients of each equipment, is the operating power of the gas boiler;

[0053] represents the CHP operating power, Indicates the operating power of the air source heat pump; Indicates the operating power of the ground source heat pump; is the operating power of the electric refrigerator; is the operating power of the absorption chiller; The operating power of the electric energy storage; is the operating power of the thermal energy storage machine; is the operating power of the fan; is the operating power of photovoltaic.

[0054]

[0055] In the formula, since this application takes into account incentive-based electric and thermal demand response, the demand response cost is mainly affected by the transferable and curtailable loads. and is the load reduction at time t; and They represent electrical load shifting,cost reduction, and thermal load reduction cost, respectively.

[0056]

[0057] In the formula, u e is the transmission cost coefficient of the power interconnection line, u h is the transmission cost coefficient of the heat interconnection line, and They represent the electric energy and thermal energy vectors traded from park n to park m respectively.

[0058] Since it involves the flow of energy between the two parties, in order to avoid adding new contradictions, it is stipulated here that the purchaser of the energy shall bear the responsibility.

[0059]

[0060] In the formula, u Ca is the carbon trading market price; and E n,t Represent actual carbon emissions and carbon emission quotas respectively.

[0061] (4) Construct the constraint model of the CIES economic operation module, including: system balance power constraint, system operation power constraint and opportunity constraint model.

[0062] (1) System balance power constraint: To ensure the operation of CIES, the CIES system is added based on the output of the system sensing module, the electric power balance based on the electric power output of each device and the electric load demand, the thermal power balance based on the thermal output of each device and the thermal load demand, and the natural gas power balance based on the natural gas consumption and output.

[0063] (2) System operation power constraints: Establish a system operation power constraint model, including the upper and lower limits of the park's electricity purchase and sale, the upper and lower limits of the thermal energy purchase and sale, and the natural gas purchase and sale volume constraints.

[0064]

[0065] In the formula, and They represent the upper limit vectors of the park’s electrical and thermal energy, respectively; Represents the upper limit vector of the amount of gas that park n purchases from the gas company at time t.

[0066] (3) Chance constraints: Considering the randomness of wind power, photovoltaic power and thermal power load in CIES, this step introduces inequality constraints in the form of probability through the chance constraint theory. These constraints will handle the balance of electric and thermal power with random variables to ensure that the constraints are met at the set confidence level. The choice of confidence level is based on the actual operation requirements of the system.

[0067] Specifically, the wind power output, photovoltaic output and load forecast values ​​are combined with uncertain variables, and the equality constraints are re-described using probability functions to adapt to the uncertainty in system operation. The original equality constraint is described as:

[0068]

[0069] Where Pr{} is the probability of an event; α and β represent its confidence level; represents the uncertainty term of the electric load, represents the uncertainty term of the heat load of the park; and They represent the reduced electric power in the park due to the uncertainty of WTG and PV respectively; To respond to the power load after the park, Respond to the post-heat load for the park; Represents the charging power of the park's electric energy storage equipment, Represents the discharge power of the electric energy storage device; Represents the charging power of the thermal energy storage equipment in the park; Represents the energy release power of the thermal energy storage device at time t.

[0070] Regarding the efficiency improvement module, in actual applications: the regulation of the CIES system and the UIES system has extremely high requirements on time. However, in a city energy network, the scale and number of nodes of UIES are extremely large, and there are many CIES. Therefore, in order to solve the problem of timeliness of regulation and optimization, the present invention adopts an ADMM algorithm with adaptive step size update of penalty factors in the efficiency improvement module, which can quickly realize the coordinated optimization of multiple parks, multiple entities and cities. The module is specifically used for: Step 1: CIES cost minimization; Step 2: UIES energy allocation; Step 3: Optimizing the adaptive step size adjustment mechanism.

[0071] (1) CIES cost minimization

[0072] 1) Confirmation of game strategy consistency: Determine whether the CIES have reached the expected condition of equal buying and selling electricity and gas volume, that is, whether the game strategy consistency is achieved. Multiple CIES negotiate on energy transactions and determine whether a consensus has been reached according to formula (14).

[0073]

[0074] In the formula, They represent the electricity purchase and sales between park n and park m respectively; They represent the heat purchase and sale between park n and park m respectively.

[0075] 2) Construction of objective function and Lagrangian augmented function: Construct the objective function of the CIES cost minimization problem and rewrite it into the form of Lagrangian augmented function. The objective function of the CIES cost minimization problem is expressed as:

[0076]

[0077] Introducing the Lagrange multiplier μ n , penalty factor θ n ; and set the maximum number of iterations k max, the objective function rewrites equation (16) into the form of Lagrangian augmented function. The objective function Lagrangian augmented function of CIES cost minimization is as follows:

[0078]

[0079] According to the Lagrange multiplier update formula, if the convergence condition is met, the iteration ends and the optimal value is output.

[0080] (2) UIES energy distribution

[0081] 1) Calculate the optimal amount of electricity and heat to be traded: Based on the solution of the CIES cost minimization phase Calculate the optimal traded electricity and heat between CIES.

[0082]

[0083] In the formula, and The optimal transaction power and heat obtained in sub-stage 1; is the total revenue from electricity and heat trading between CIES; The electricity price and heat price to be paid for the electric energy to be solved in sub-stage 2 are respectively.

[0084] 2) Construction of objective function and Lagrangian augmented function: Set the objective function of UIES energy allocation and rewrite it in the form of Lagrangian augmented function.

[0085]

[0086] In the formula, The optimal operating cost of each CIES calculated for the CIES cost minimum stage; It is the operating cost of each CIES when no bargaining is carried out.

[0087] Introducing auxiliary variables Promote the decoupling of energy transaction prices between CIES. Formula (19) indicates that when the electricity price and heat price that each CIES hopes to pay for purchasing energy from other CIES are equal to the electricity price and heat price paid for selling electricity, sub-stage 2 completes the fair decision of energy payment price.

[0088]

[0089] Setting the Lagrange multiplier Penalty factor φ n,m ; Introduce (19) and rewrite it into Lagrangian augmented function.

[0090] The Lagrangian augmented function of the payment price allocation objective function is as follows:

[0091]

[0092] The update is performed according to the Lagrange multiplier update formula. If the convergence condition is met, the iteration ends and the optimal value is output.

[0093] (3) Optimize the adaptive step-size adjustment mechanism.

[0094] 1) Formulation of step size adjustment strategy: flexibly adjust the step size ρ according to the size of the residual k , the saturation of the logarithmic function is used to avoid excessive adjustment and ensure the stable convergence of the algorithm. The step size of k+1 iterations can be expressed as ρ k+1 .

[0095]

[0096] 2) Step size adjustment implementation: According to the ratio of the original residual and the dual residual, an adaptive strategy is used to adjust the step size of the algorithm. This helps to balance the convergence speed of the original residual and the dual residual, and optimize the performance and convergence time of the algorithm. The basic idea of ​​the above adaptive adjustment mechanism is to balance the original residual s k and the dual residual r k Convergence speed, to avoid slow convergence due to one of the two. When the original residual is relatively large, increasing the step size can increase the proportion of the norm term in equation (22), promote the approach of x to the optimal solution, and thus speed up convergence; when the original residual is relatively small, reducing the step size can reduce the oscillation of the objective function and promote the convergence of the variables. This paper takes s k and r k The logarithm of the ratio between them is used as a reference for adjusting the step size. While the step size is flexibly adjusted according to the residual size comparison, the saturation of the logarithmic function can be used to effectively avoid the non-convergence problem of the algorithm iteration process caused by excessive step size adjustment.

[0097] A case study was conducted using a UIES in northern China. The case study takes 24 hours as the planning time and the time step is 1 hour. The upper layer is based on the IEEE 33-node distribution network and the 23-node radiation-type heating network as the network setting. Four scenarios are considered: Scenario 1: Neither the upper nor the lower layers consider game participation, that is, the power grid and the gas grid directly supply energy; Scenario 2: UIES considers security and CIES does not participate in cooperation; Scenario 3: The upper UIES does not consider security, and the lower CIES considers cooperation; Scenario 4: UIES considers security and CIES considers cooperative operation.

[0098]

[0099] As can be seen from the table, whether UIES considers safe operation or the cooperation between multiple CIES at the lower level, the energy operation cost will be significantly reduced. For example, from scenario 1 to scenario 2, CIES1, CIES2 and CIES3 were reduced by 1062.8, 745.9 and 1392.1 yuan respectively, so that both the upper and lower levels benefited; in scenario 3, only cooperation was adopted, and the upper UIES safe operation was not adopted, so the UIES benefit was 0, but after cooperation, the lower CIES reduced the cost by 1453.2, 4416, and 917.2 yuan, that is, benefited. By comparing scenario 2 with scenario 4, it can also be proved that after adopting the cooperation method, the CIES cost is reduced, and even the economic benefits of UIES and CIES will be improved. Compared with the original operation of scenario 1, UIES benefited by 62914.65 yuan, and the total CIES cost was reduced to 85.77% of the original cost, a reduction of 14.23%, with a significant improvement effect. Figure 2 A schematic diagram of iterative optimization of energy trading using the method is shown in detail. Figure 3 The energy trading situation of the system when the method converges is given.

[0100] In addition, the present application also provides a security optimization method using the above-mentioned device. Please refer to the above content and will not repeat it here.

[0101] The optional implementation modes of the embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation modes. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical scheme of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

Claims

1. A two-level energy system security optimization device for an urban park, characterized in that: It includes system perception module, energy security analysis module, economic operation module and efficiency improvement module; The system perception module is used for real-time status perception and monitoring of the urban integrated energy system UIES and the park integrated energy system CIES; The energy security analysis module is used to formulate the best energy security operation strategy of UIES with UIES as the main body; The economic operation module is used to take CIES as the main body, treat a single CIES as an independent operation subject, and formulate specific economic operation goals and corresponding system constraints; The efficiency improvement module is used to achieve the coordinated optimization of multiple parks, multiple entities and cities; The energy security analysis module is specifically used for: (1) Establish the optimal operation objective function of the energy security analysis module, including the lowest energy operation risk and the lowest energy sales; (2) Determine the operational feasibility boundary constraints of UIES based on the data output of the system perception module; The optimal operation objective function: Where F is the total economic-safety benefit of the entire UIES; n is the integrated energy system of a certain park; t is the time when the system is running, with a step length of hours; T is the total time when the system is running; and They represent the electricity sales price of UIES to CIES and the large power grid at time t, and They represent the electricity purchase price of UIES from CIES and the large power grid at time t respectively; They represent the electricity traded between UIES and CIES at time t and the electricity traded with the large power grid. represents the operating load loss cost of the system; The operational feasibility boundary constraints of the UIES include the following: (1) Constraints on electricity purchase and sales prices: In order to prevent unreasonable price fluctuations and excessively high electricity prices, electricity prices are required to fluctuate within a reasonable range. The specific electricity price constraints are shown in the formula: In the formula, Represents the price transaction vector of UIES, and The upper and lower limit vectors representing the UIES transaction price; (2) Price fluctuation control: impose constraints on the maximum average purchase and sale price of electricity, as shown in formula (3): In the formula, is the maximum average purchase and sale price vector of electricity that UIES gives to each CIES; In the economic operation module, the economic objective function is set as: Where F(C) represents the CIES economic objective function, Represents the system gas purchase cost; represents the transaction costs with UIES; represents the energy transaction cost between various parks at time t; Represents the operating cost of the system equipment; represents the demand response cost; The network fee represents the energy; represents the cost of carbon trading; In the economic operation module, the corresponding system constraints include: system balance power constraints, system operation power constraints and opportunity constraint models; System balance power constraints: electric power balance based on the electric power output of each device and the electric load demand, thermal power balance based on the thermal output of each device and the thermal load demand, and natural gas power balance based on natural gas consumption and output; Chance constraints: Introducing inequality constraints in the form of probabilistic constraints through the theory of chance constraints; System operation power constraint: Establish a system operation power constraint model, including upper and lower limit constraints on the purchase and sale of electricity in the park, upper and lower limit constraints on the purchase and sale of thermal energy, and natural gas purchase and sale volume constraints In the formula, and They represent the upper limit vectors of the park’s electrical and thermal energy, respectively; Represents the upper limit vector of the amount of gas that park n purchases from the gas company at time t.

2. According to claim 1, a two-level energy system security optimization device for a city park is characterized in that: The system perception module is specifically used for: (1) Collect key data from UIES and CIES. The collected key data are preprocessed to remove noise and standardize to obtain processed data; (2) Eliminate outliers from the processed data; (3) The data after outliers are eliminated are sent to the energy security analysis module and the economic operation module.

3. The urban park two-level energy system safety optimization device according to claim 2 is characterized in that: The system perception module is equipped with a dynamic feedback system that can fit historical data with the data collected by the system perception module and perform reliability analysis, and automatically adjust the data collection frequency and accuracy based on the analysis results, thereby optimizing the perception efficiency and response speed of the entire system.

4. A method for optimizing the safety of a two-level energy system in an urban park, characterized in that: The method is carried out using the device according to any one of claims 1 to 3.

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