Consensus-based seaport virtual power plant cooperative operation decentralized scheduling method

Abstract

The invention belongs to the technical field of power dispatching, and provides a seaport virtual power plant cooperative operation decentralized dispatching method based on consensus, comprising the following steps: S1, constructing a seaport virtual power plant system model SVPP; s2, constructing an energy service model; and S3, designing a decentralized scheduling method of the seaport virtual power plant. According to the method, serious challenges caused by accelerated electrification of a seaport are solved, the overall potential and flexibility of SVPP are enhanced, meanwhile, privacy of all related entities is protected, energy interaction between the coast side and the ship side is enhanced, and people can sell energy to the coast side and can also participate in mutual energy sharing.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power dispatching, and in particular relates to a consensus-based decentralized dispatching method for collaborative operation of a seaport virtual power plant. Background Art

[0002] The establishment of shore power infrastructure and the electrification of port equipment and ships have promoted the deep integration between multiple entities in the seaport energy system. Traditionally, cranes and auxiliary generators of ships are powered by diesel and other fossil fuels. In recent years, many seaports have been equipped with shore power systems and gradually transitioned to electrification. Shore power systems of varying degrees have been widely used in seaports around the world. Although the motivations for electrification vary from port to port, they all contribute to the expansion of energy services in seaports, supporting economic operations and environmental sustainability.

[0003] However, the increase in seaport electrification places greater demands on the scheduling and management of energy systems. Existing studies only consider electrified ships as temporary loads during berthing, ignoring their value as flexible energy sources. In addition, the aggregation of extensive marine resources increases the complexity of system management. At the same time, existing studies require that all entities within a virtual power plant (VPP) be operated by a centralized operator. In SVPPs, mutual distrust is prevalent between different ownership entities due to privacy protection. This distrust makes centralized approaches impractical when information sharing is limited. Therefore, the communication mechanism of a decentralized approach can be used to reduce reliance on global information and provide opportunities for flexible energy trading between multiple entities.

[0004] The essence of decentralized methods is to decouple the entire optimization problem into several manageable local problems, which are then solved in parallel and independently. Existing decomposition methods are generally divided into distributed methods and decentralized methods. Distributed methods require a central coordinator to collect and return key information from each local system to complete the decision-making process of the entire system. This distributed framework does not allow direct information exchange between local systems, but a central coordinator optimizes key variables. Commonly used distributed methods include dual decomposition based on Lagrangian relaxation and alternating direction method of multipliers (ADMM) based on augmented Lagrangian relaxation. It is worth noting that the quadratic penalty term is considered in the augmented Lagrangian relaxation process to improve the convexity of the function and improve the convergence of the algorithm. Decentralized methods run without a central coordinator for overall decision-making, but each local system directly exchanges information with its connected neighbors. This approach further decouples the entire energy system and ensures personal privacy security. Given these advantages, decentralized architectures are considered to be more suitable for decision-making and control in SVPPs with multi-entity interactions.

[0005] Existing decentralized approaches mainly focus on energy management of static entities. However, managing the energy of dynamic and flexible entities is more complex. This complexity stems from the frequent movement of ships, the different energy requirements of port equipment, and the different energy transfer patterns between port equipment and ships. Therefore, there is an urgent need to develop a decentralized scheduling approach in SVPP to address the significant increase in data exchange and the interruption of communication connections between ships and shore. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a consensus-based decentralized scheduling method for the coordinated operation of a seaport virtual power plant, aiming to utilize the flexibility value of ships and promote the design and operation of seaport energy systems. The present invention combines V2S and V2V mechanisms, which not only promotes energy transactions between the coast and ships, but also directly promotes energy transactions between ships, reducing the overall energy cost of SVPP. Considering the privacy issues of multiple entities in SVPP, the scheduling method of the present invention optimizes each entity independently and in parallel.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The consensus-based decentralized dispatching method for collaborative operation of seaport virtual power plants includes the following steps:

[0009] S1. Construct the seaport virtual power plant system model SVPP;

[0010] The seaport virtual power plant system model is formed by aggregating distributed energy and power equipment through power lines;

[0011] The electrical equipment includes electrified ships, dock cranes and refrigerated containers;

[0012] S2. Build energy service model;

[0013] Taking advantage of the fluctuations in electricity prices between the seaport and the main power grid, SVPP maximizes profits and minimizes costs through energy trading, which can promote the specific charging needs of ships and reduce the energy expenditure of ships;

[0014] S3. Design a decentralized dispatching method for seaport virtual power plants.

[0015] Furthermore, the seaport virtual power plant system model constructed in step S1 has the following characteristics:

[0016] The boundaries of the seaport extend from the traditional shore side to both shore side and ship side components;

[0017] The shore power facility establishes a two-way flow channel between the SVPP and the electrified ship, allowing the ship to optimize energy dispatch according to fluctuations in electricity prices;

[0018] Ships are electrified ships, all equipped with ESS, which have dual functions, serving as both charging loads and energy storage devices. They can be regarded as a small-scale microgrid, that is, through the two-way coastal power system, ships can consume electricity and transfer stored energy back to the shore or the power grid;

[0019] Adopt an intermittent power supply strategy to adjust the storage temperature of the refrigerated container, thereby limiting the peak consumption of the refrigerated container; make the refrigerated container operate within the allowable temperature control range, and once the temperature reaches the allowable upper limit threshold, the cooling system will be activated to reduce the storage temperature to a safe storage range, saving energy;

[0020] Purchase electricity from the main grid at market prices, shift electricity consumption to off-peak periods, thereby reducing consumption during peak periods; introduce renewable energy, which comes from photovoltaic and wind distributed generation units;

[0021] Build a shore-based energy storage system.

[0022] Furthermore, the shore-based energy storage system stores energy during periods of low electricity prices and releases energy during periods of high electricity prices.

[0023] Further, the energy service model includes S2G services, V2S and V2V energy transactions;

[0024] The S2G service is a time-decoupled service that promotes decarbonization and enables ships to sell energy back to the main grid, using energy from ESS and other sources to increase profits and reduce costs; it prevents the simultaneous buying and selling of electricity from the main grid, thus avoiding inefficient transmission;

[0025] V2S energy trading refers to the provision of energy services by ships to port facilities;

[0026] V2V energy trading is when moored ships are able to exchange energy with other ships, reducing demand on the main grid.

[0027] Further, the step S3 includes the following sub-steps:

[0028] S3.1 Framework for decentralized scheduling;

[0029] S3.2 Design a consensus-based decentralized energy management algorithm.

[0030] Furthermore, the framework of the decentralized scheduling includes initialization of entities, local optimization of entities, and information exchange between entities;

[0031] Initialization of entities: In SVPP, set the initial parameters of each entity, including the efficiency and boundaries of onshore energy storage and energy conversion equipment, distribution system line parameters, load conditions, container battery data, and optimization algorithm configuration;

[0032] Local optimization of entities: treating the coast and each ship as different autonomous entities, thus facilitating a parallel and independent optimization process;

[0033] Information exchange between entities: Support collaboration across multiple entities while protecting privacy.

[0034] Further, the step S3.2 includes the following sub-steps:

[0035] The entire coast and each ship are regarded as autonomous entities, and the SVPP is defined to consist of V+1 entities, represented as n∈N={1,2,…,N}. The V2V transaction between ships is extended to the energy transaction between N entities including the coast, thus realizing the power balance constraint in the equation. The calculation expression is:

[0036]

[0037] Where P et n,m is the energy purchased by entity n from entity m in time period t, determined as a coupling variable; d qc represents the demand for quay crane demand response; d rc Indicates the power consumption after intermittent power supply; p g Indicates the main power grid power; p re represents the power supplied by renewable energy in time period t; p essc represents the charging power at time t; p essd represents the discharge power at time t; p s2g It represents the amount of electricity sold by SVPP to the main grid;

[0038] The transaction costs between entities are defined as:

[0039]

[0040] Among them, π et It is a fixed transaction price;

[0041] The operational optimization problem of SVPP is regarded as minimizing the operating cost and transaction cost of each entity, which can be expressed as follows:

[0042]

[0043] In SVPP, consensus-based ADMM is used to perform decentralized energy management. en t ,m,t and the dual variable λ n,m,t , each entity only needs to reach a consensus with its neighboring entities to ensure the privacy of the entity. The augmented Lagrangian function of the optimization problem is recorded as

[0044]

[0045] Consensus variables should satisfy the following energy trading constraints:

[0046]

[0047] The optimal solution for the consensus variable is as follows:

[0048]

[0049] Where the positive parameter ρ is the step size of the augmented Lagrangian function.

[0050] The beneficial effects of the present invention are:

[0051] The present invention addresses the serious challenges posed by the accelerated electrification of seaports, which is a critical step in achieving the transition to near-zero seaports. In order to manage the complex integration of multiple flexibility resources, the present invention proposes a consensus-based decentralized energy scheduling method. This method enhances the overall potential and flexibility of SVPPs while protecting the privacy of all relevant entities. Specifically, the present invention takes advantage of the unique advantages of electrified ships as mobile energy storage units and incorporates both V2S and V2V transmission modes into the SVPP model. This combination strengthens the energy interaction between the shore side and the ship side, enabling people to not only sell energy to the shore side, but also participate in mutual energy sharing.

[0052] This invention demonstrates that ship energy trading has significant flexibility and potential in energy storage and supply. A particularly noteworthy result is that under V2S trading conditions alone, the energy costs of ships with longer berthing times are more significantly reduced. However, with the introduction of V2V trading, these ships sacrificed their individual cost benefits to further improve the collective benefits of SVPP. This phenomenon challenges the traditional belief that longer berthing times themselves will save costs and highlights the complex interaction between individual ship optimization and overall system efficiency. In addition, SVPP defines the operations of entities based on the ownership of resources and decouples the optimization process of each entity by introducing consensus variables. The optimization only shares key information, thereby protecting the privacy and security of different entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The structure of the virtual power plant for the seaport;

[0054] Figure 2 A framework for decentralized scheduling;

[0055] Figure 3 The operation process of the consensus-based decentralized energy management algorithm. DETAILED DESCRIPTION

[0056] In order to make the technical solutions, advantages and purposes of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of this application.

[0057] like Figure 1 to Figure 3 As shown, the present invention provides a consensus-based seaport virtual power plant collaborative operation decentralized scheduling method, comprising the following steps:

[0058] S1. Construct the seaport virtual power plant system model SVPP;

[0059] The seaport virtual power plant system model is formed by aggregating distributed energy resources and power equipment through power lines;

[0060] Power equipment including electrified ships, quay cranes and refrigerated containers;

[0061] The seaport virtual power plant system model constructed in step S1 has the following characteristics:

[0062] The boundaries of the seaport extend from the traditional shore side to both shore side and ship side components;

[0063] The shore power facility establishes a two-way flow channel between the SVPP and the electrified ship, allowing the ship to optimize energy dispatch according to fluctuations in electricity prices;

[0064] Ships are electrified ships, all equipped with ESS, which have dual functions, serving as both charging loads and energy storage devices. They can be regarded as a small-scale microgrid, that is, through the two-way coastal power system, ships can consume electricity and transfer stored energy back to the shore or the power grid;

[0065] Adopt an intermittent power supply strategy to adjust the storage temperature of the refrigerated container, thereby limiting the peak consumption of the refrigerated container; make the refrigerated container operate within the allowable temperature control range, and once the temperature reaches the allowable upper limit threshold, the cooling system will be activated to reduce the storage temperature to a safe storage range, saving energy;

[0066] Purchase electricity from the main grid at market prices, shifting electricity consumption to off-peak periods, thereby reducing consumption during peak periods; introduce renewable energy, which comes from photovoltaic and wind distributed generation units;

[0067] A shore-based energy storage system is built to store energy during periods of low electricity prices and release energy during periods of high electricity prices.

[0068] S2. Build energy service model;

[0069] Taking advantage of the fluctuations in electricity prices between the seaport and the main power grid, SVPP maximizes profits and minimizes costs through energy trading, which can promote the specific charging needs of ships and reduce the energy expenditure of ships;

[0070] Energy service models include S2G services, V2S and V2V energy trading;

[0071] The S2G service is a time-decoupled service that promotes decarbonization and enables ships to sell energy back to the main grid, using energy from ESS and other sources to increase profits and reduce costs; it prevents the simultaneous buying and selling of electricity from the main grid, thus avoiding inefficient transmission;

[0072] V2S energy trading refers to the provision of energy services by ships to port facilities;

[0073] V2V energy trading is when moored ships are able to exchange energy with other ships, reducing demand on the main grid.

[0074] S3. Design a decentralized dispatching method for the seaport virtual power plant;

[0075] S3.1 Framework for decentralized scheduling;

[0076] The framework of decentralized scheduling includes entity initialization, entity local optimization, and information exchange between entities;

[0077] Initialization of entities: In SVPP, set the initial parameters of each entity, including the efficiency and boundaries of onshore energy storage and energy conversion equipment, distribution system line parameters, load conditions, container battery data, and optimization algorithm configuration;

[0078] Local optimization of entities: treating the coast and each ship as different autonomous entities, thus facilitating a parallel and independent optimization process;

[0079] Information exchange between entities: Support collaboration across multiple entities while protecting privacy;

[0080] S3.2 Design a consensus-based decentralized energy management algorithm;

[0081] Step S3.2 includes the following sub-steps:

[0082] The entire coast and each ship are regarded as autonomous entities, and the SVPP is defined to consist of V+1 entities, represented as n∈N={1,2,…,N}. The V2V transaction between ships is extended to the energy transaction between N entities including the coast, thus realizing the power balance constraint in the equation. The calculation expression is:

[0083]

[0084] In the formula, is the energy purchased by entity n from entity m in time period t, determined as a coupling variable; d qc represents the demand for quay crane demand response; d rc Indicates the power consumption after intermittent power supply; p g Indicates the main power grid power; p re represents the power supplied by renewable energy in time period t; p essc represents the charging power at time t; p essd represents the discharge power at time t; p s2g It represents the amount of electricity sold by SVPP to the main grid;

[0085] The transaction costs between entities are defined as:

[0086]

[0087] Among them, π et It is a fixed transaction price;

[0088] The operational optimization problem of SVPP is regarded as minimizing the operating cost and transaction cost of each entity, which can be expressed as follows:

[0089]

[0090] In SVPP, consensus-based ADMM is used for decentralized energy management. and the dual variable λ n,m,t , each entity only needs to reach a consensus with its neighboring entities to ensure the privacy of the entity. The augmented Lagrangian function of the optimization problem is recorded as

[0091]

[0092] Consensus variables should satisfy the following energy trading constraints:

[0093]

[0094] The optimal solution for the consensus variable is as follows:

[0095]

[0096] Where the positive parameter ρ is the step size of the augmented Lagrangian function.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the protection scope of the present invention.

Claims

1. A consensus-based decentralized dispatching method for collaborative operation of a seaport virtual power plant, characterized in that: The following steps are involved: S1. Construct the seaport virtual power plant system model SVPP; The seaport virtual power plant system model is formed by aggregating distributed energy and power equipment through power lines; The electrical equipment includes electrified ships, dock cranes and refrigerated containers; S2. Build energy service model; Taking advantage of the fluctuations in electricity prices between the seaport and the main power grid, SVPP maximizes profits and minimizes costs through energy trading, which can promote the specific charging needs of ships and reduce the energy expenditure of ships; S3. Design a decentralized dispatching method for seaport virtual power plants.

2. According to a consensus-based decentralized scheduling method for coordinated operation of a seaport virtual power plant according to claim 1, it is characterized by: The seaport virtual power plant system model constructed in step S1 has the following characteristics: The boundaries of the seaport extend from the traditional shore side to both shore side and ship side components; The shore power facility establishes a two-way flow channel between the SVPP and the electrified ship, allowing the ship to optimize energy dispatch according to fluctuations in electricity prices; Ships are electrified ships, all equipped with ESS, which have dual functions, serving as both charging loads and energy storage devices. They can be regarded as a small-scale microgrid, that is, through the two-way coastal power system, ships can consume electricity and transfer stored energy back to the shore or the power grid; Adopting intermittent power supply strategy to adjust the storage temperature of refrigerated containers, thereby limiting the peak consumption of refrigerated containers; The refrigerated container is operated within the permissible temperature control range. Once the temperature reaches the permissible upper threshold, the cooling system will be activated to reduce the storage temperature to a safe storage range, saving energy. Purchase electricity from the main grid at market prices, shift electricity consumption to off-peak periods, thereby reducing consumption during peak periods; introduce renewable energy, which comes from photovoltaic and wind distributed generation units; Build a shore-based energy storage system.

3. The consensus-based decentralized dispatching method for coordinated operation of a seaport virtual power plant according to claim 2 is characterized in that: The shore-based energy storage system stores energy during periods of low electricity prices and releases energy during periods of high electricity prices.

4. The consensus-based decentralized dispatching method for coordinated operation of a seaport virtual power plant according to claim 1 is characterized by: The energy service model includes S2G services, V2S and V2V energy transactions; The S2G service is a time-decoupled service that promotes decarbonization and enables ships to sell energy back to the main grid, increasing profits and reducing costs using energy obtained from ESS and other sources; Prevents simultaneous buying and selling of electricity from the main grid, thus avoiding inefficient transmission; V2S energy trading refers to the provision of energy services by ships to port facilities; V2V energy trading is when moored ships are able to exchange energy with other ships, reducing demand on the main grid.

5. The consensus-based decentralized dispatching method for coordinated operation of a seaport virtual power plant according to claim 1 is characterized by: The step S3 comprises the following sub-steps: S3.1 Framework for decentralized scheduling; S3.2 Design a consensus-based decentralized energy management algorithm.

6. The consensus-based decentralized dispatching method for coordinated operation of a seaport virtual power plant according to claim 5 is characterized by: The framework of the decentralized scheduling includes entity initialization, entity local optimization, and information exchange between entities; Initialization of entities: In SVPP, set the initial parameters of each entity, including the efficiency and boundaries of onshore energy storage and energy conversion equipment, distribution system line parameters, load conditions, container battery data, and optimization algorithm configuration; Local optimization of entities: treating the coast and each ship as different autonomous entities, thus facilitating a parallel and independent optimization process; Information exchange between entities: Support collaboration across multiple entities while protecting privacy.

7. The consensus-based decentralized dispatching method for coordinated operation of a seaport virtual power plant according to claim 5 is characterized by: The step S3.2 includes the following sub-steps: The entire coast and each ship are regarded as autonomous entities, and the SVPP is defined to consist of V+1 entities, represented as n∈N={1,2,…,N}. The V2V transaction between ships is extended to the energy transaction between N entities including the coast, thus realizing the power balance constraint in the equation. The calculation expression is: In the formula, is the energy purchased by entity n from entity m in time period t, determined as a coupling variable; d qc represents the demand for quay crane demand response; d rc Indicates the power consumption after intermittent power supply; p g Indicates the main power grid power; p re represents the power supplied by renewable energy in time period t; p essc represents the charging power at time t; p essd represents the discharge power at time t; p s2g It represents the amount of electricity sold by SVPP to the main grid; The transaction costs between entities are defined as: Among them, π et It is a fixed transaction price; The operational optimization problem of SVPP is regarded as minimizing the operating cost and transaction cost of each entity, which can be expressed as follows: In SVPP, consensus-based ADMM is used for decentralized energy management. and the dual variable λ n,m,t , each entity only needs to reach a consensus with its neighboring entities to ensure the privacy of the entity. The augmented Lagrangian function of the optimization problem is recorded as Consensus variables should satisfy the following energy trading constraints: The optimal solution for the consensus variable is as follows: Where the positive parameter ρ is the step size of the augmented Lagrangian function.

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