Consensus-based collaborative operation and decentralized scheduling method for harbor virtual power plant

Abstract

The present application belongs to the technical field of power dispatching, and provides a consensus-based harbor virtual power plant collaborative operation decentralized scheduling method, which comprises the following steps: S1. constructing a harbor virtual power plant system model SVPP; S2. constructing an energy service model; and S3. designing a decentralized scheduling method for the harbor virtual power plant. The present application solves the serious challenges brought by the acceleration of harbor electrification, enhances the overall potential and flexibility of the SVPP, protects the privacy of all related entities, strengthens the energy interaction between the coast side and the ship side, enables people not only to sell energy to the coast side, but also to participate in mutual energy sharing.

Description

Technical Field

[0001] This invention belongs to the field of power dispatching technology, specifically relating to a consensus-based decentralized dispatching method for collaborative operation of virtual power plants in seaports. Background Technology

[0002] The establishment of shore power infrastructure and the electrification of port facilities and vessels have facilitated deeper integration among multiple entities within the port energy system. Traditionally, cranes and auxiliary generators on ships are powered by diesel and other fossil fuels. In recent years, many seaports have been equipped with shore power systems, gradually transitioning to electrification, and varying degrees of shore power systems are widely used in seaports around the world. While the motivations for electrification differ from port to port, they all contribute to the expansion of port energy services, supporting economic operations and environmental sustainability.

[0003] However, the increasing electrification of seaports places greater demands on the scheduling and management of energy systems. Existing research only treats electrified vessels as temporary loads during berthing, neglecting their value as flexible energy sources. Furthermore, the aggregation of extensive marine resources increases the complexity of system management. Simultaneously, existing research requires all entities within a Virtual Power Plant (VPP) to be operated by a centralized operator. In VPPs, mutual distrust is prevalent among different ownership entities due to privacy concerns. This distrust makes a centralized approach impractical when information sharing is limited. Therefore, decentralized communication mechanisms can be utilized to reduce reliance on global information and provide opportunities for flexible energy trading among 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 and decentralized methods. Distributed methods require a central coordinator to collect and return key information from each local system to complete the overall system decision-making process. This distributed framework does not allow direct information exchange between local systems, but a central coordinator optimizes the key variables. Commonly used distributed methods include dual decomposition based on Lagrange relaxation and the Alternating Multiplier Direction Method (ADMM) based on augmented Lagrange relaxation. Notably, a quadratic penalty term is considered in the augmented Lagrange relaxation process to improve the convexity of the function and the convergence of the algorithm. Decentralized methods do not have a central coordinator making overall decisions; instead, each local system directly exchanges information with its connected neighbors. This approach further decouples the entire energy system and ensures individual privacy. Given these advantages, decentralized architectures are considered more suitable for decision-making and control in SVPPs with multi-entity interactions.

[0005] Existing decentralized approaches primarily focus on energy management for static entities. However, managing the energy of dynamic and flexible entities is far more complex. This complexity stems from frequent ship movement, varying energy demands of port equipment, and differing energy transfer patterns between port equipment and ships. Therefore, there is an urgent need to develop a decentralized scheduling method for SVPP to address issues such as the significant increase in data exchange and disruptions to communication connections between ships and the shore. Summary of the Invention

[0006] Therefore, the purpose of this invention is to provide a consensus-based decentralized scheduling method for the collaborative operation of a port virtual power plant (SVPP), aiming to leverage the flexibility of ships to advance the design and operation of port energy systems. This invention combines V2S and V2V mechanisms, facilitating not only energy transactions between the coast and ships but also directly among ships, thus reducing the overall energy cost of the SVPP. Considering the privacy concerns of multiple entities in the SVPP, the scheduling method of this invention optimizes each entity independently and in parallel.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A consensus-based decentralized scheduling method for collaborative operation of virtual power plants in seaports includes the following steps:

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

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

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

[0012] S2. Construct an energy service model;

[0013] By leveraging the electricity price fluctuations between seaports and the main power grid, SVPP maximizes profits and minimizes costs through energy trading, thereby promoting specific charging needs of ships and reducing ship energy expenditures.

[0014] S3. Design a distributed scheduling method for a virtual power plant in a seaport.

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

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

[0017] Shore power facilities establish a two-way flow channel between SVPPs and electrified vessels, allowing vessels to optimize energy dispatch based on electricity price fluctuations;

[0018] The ships are all electrified and equipped with ESS (Energy Storage System), which has a dual function: it can serve as a charging load and an energy storage device. It can be regarded as a small-scale microgrid. That is, through the bidirectional coastal power system, the ships can both consume electricity and transfer the stored energy back to the shore or the power grid.

[0019] An intermittent power supply strategy is adopted to regulate the storage temperature of refrigerated containers, thereby limiting the peak consumption of refrigerated containers; the refrigerated containers are kept within the allowable temperature control range, and once the temperature reaches the upper limit threshold, the cooling system is activated to reduce the storage temperature to a safe storage range, thus saving energy.

[0020] Electricity is purchased from the main grid at market prices, and electricity consumption is shifted to off-peak periods, thereby reducing consumption during peak periods; renewable energy sources are introduced, which come from photovoltaic power generation and wind power distributed generation units;

[0021] Construct onshore energy storage systems.

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

[0023] Furthermore, the energy service model includes S2G services, V2S and V2V energy trading;

[0024] S2G service is a time-decoupling service that promotes decarbonization, enabling ships to sell energy back to the main grid and use energy from ESS and other sources to increase profits and reduce costs; it also prevents simultaneous buying and selling of electricity from the main grid, thereby avoiding inefficient transmission.

[0025] V2S energy trading refers to ships providing energy services to port facilities;

[0026] V2V energy trading refers to the ability of moored ships to exchange energy with other ships, thereby reducing the demand on the main power grid.

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

[0028] S3.1 Framework for Distributed Scheduling;

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

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

[0031] Entity initialization: In SVPP, set the initial parameters for each entity, including the efficiency and boundary of onshore energy storage and energy conversion equipment, power 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 facilitates a parallel and independent optimization process;

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

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

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

[0036]

[0037] In the formula, P et n,m The energy purchased by entity n from entity m during time period t is defined as the coupling variable; d qc This indicates the demand response of the dock cranes; d rc p represents the power consumption after using intermittent power supply. g p represents the main power grid power; re p represents the power supplied by renewable energy during the time period t; essc p represents the charging power at time t; essd p represents the discharge power at time t; s2g This indicates the amount of electricity sold by SVPP to the main grid;

[0038] Transaction costs between entities are defined as:

[0039]

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

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

[0042]

[0043] In SVPP, a consensus-based ADMM is used for decentralized energy management by introducing a consensus variable p^ en t ,m,t and dual variable λ n,m,t Each entity only needs to reach a consensus with its neighboring entities to guarantee entity privacy. The augmented Lagrangian function for the optimization problem is denoted as...

[0044]

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

[0046]

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

[0048]

[0049] In the formula, the positive parameter ρ is the step size of the augmented Lagrange function.

[0050] The beneficial effects of this invention are as follows:

[0051] This invention addresses the significant challenges posed by the accelerated electrification of seaports, a crucial step towards a near-zero seaport transition. To manage the complex integration of various flexible resources, this invention proposes a consensus-based decentralized energy dispatch method. This method enhances the overall potential and flexibility of SVPP (Ship-to-Port Power) while protecting the privacy of all relevant entities. Specifically, this invention leverages the unique advantage of electrified ships as mobile energy storage units, simultaneously incorporating V2S and V2V transmission modes into the SVPP model. This combination strengthens energy interaction between the shore-side and ship-side, enabling not only the sale of energy to the shore but also participation in mutual energy sharing.

[0052] This invention demonstrates the significant flexibility and potential of ship energy trading in energy storage and supply. A particularly noteworthy result is that, under V2S trading conditions alone, the energy costs for ships with longer berthing times are significantly reduced. However, with the introduction of V2V trading, these ships sacrifice individual cost benefits to further enhance the collective benefit of SVPP. This phenomenon challenges the conventional wisdom that longer berthing times themselves save costs and highlights the complex interplay between individual ship optimization and overall system efficiency. Furthermore, SVPP defines entity operations based on resource ownership and decouples the optimization process for each entity by introducing consensus variables. This optimization shares only critical information, thus protecting the privacy and security of different entities. Attached Figure Description

[0053] Figure 1 The structure of a virtual power plant in a seaport;

[0054] Figure 2 A framework for distributed scheduling;

[0055] Figure 3 This describes the operation process of a consensus-based decentralized energy management algorithm. Detailed Implementation

[0056] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0057] like Figures 1-3 As shown, this invention provides a consensus-based distributed scheduling method for collaborative operation of a virtual power plant in a seaport, comprising the following steps:

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

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

[0060] Electrical equipment includes electrified ships, dock cranes, and refrigerated containers;

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

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

[0063] Shore power facilities establish a two-way flow channel between SVPPs and electrified vessels, allowing vessels to optimize energy dispatch based on electricity price fluctuations;

[0064] The ships are all electrified and equipped with ESS (Energy Storage System), which has a dual function: it can serve as a charging load and an energy storage device. It can be regarded as a small-scale microgrid. That is, through the bidirectional coastal power system, the ships can both consume electricity and transfer the stored energy back to the shore or the power grid.

[0065] An intermittent power supply strategy is adopted to regulate the storage temperature of refrigerated containers, thereby limiting the peak consumption of refrigerated containers; the refrigerated containers are kept within the allowable temperature control range, and once the temperature reaches the upper limit threshold, the cooling system is activated to reduce the storage temperature to a safe storage range, thus saving energy.

[0066] Purchase electricity from the main grid at market prices and shift electricity consumption to off-peak periods to reduce consumption during peak hours; introduce renewable energy sources, namely photovoltaic power generation and wind power distributed generation units.

[0067] An onshore energy storage system has been constructed, which stores energy during periods of low electricity prices and releases energy during periods of high electricity prices.

[0068] S2. Construct an energy service model;

[0069] By leveraging the electricity price fluctuations between seaports and the main power grid, SVPP maximizes profits and minimizes costs through energy trading, thereby promoting specific charging needs of ships and reducing ship energy expenditures.

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

[0071] S2G service is a time-decoupling service that promotes decarbonization, enabling ships to sell energy back to the main grid and use energy from ESS and other sources to increase profits and reduce costs; it also prevents simultaneous buying and selling of electricity from the main grid, thereby avoiding inefficient transmission.

[0072] V2S energy trading refers to ships providing energy services to port facilities;

[0073] V2V energy trading refers to the ability of moored ships to exchange energy with other ships, thereby reducing the demand on the main power grid.

[0074] S3. Design a distributed scheduling method for a virtual power plant in a seaport;

[0075] S3.1 Framework for Distributed Scheduling;

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

[0077] Entity initialization: In SVPP, set the initial parameters for each entity, including the efficiency and boundary of onshore energy storage and energy conversion equipment, power 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 facilitates a parallel and independent optimization process;

[0079] Information exchange between entities: Supports 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] Treating the entire coastline and each ship as autonomous entities, the SVPP is defined as consisting of V+1 entities, represented as n∈N={1,2,……,N}. The V2V transactions between ships are extended to energy transactions between N entities, including the coastline, thus realizing the power balance constraint in the equation. The calculation expression is:

[0083]

[0084] In the formula, The energy purchased by entity n from entity m during time period t is defined as the coupling variable; d qc This indicates the demand response of the dock cranes; d rc p represents the power consumption after using intermittent power supply. g p represents the main power grid power; re p represents the power supplied by renewable energy during the time period t; essc p represents the charging power at time t; essd p represents the discharge power at time t; s2g This indicates the amount of electricity sold by SVPP to the main grid;

[0085] Transaction costs between entities are defined as:

[0086]

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

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

[0089]

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

[0091]

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

[0093]

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

[0095]

[0096] In the formula, the positive parameter ρ is the step size of the augmented Lagrange function.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A consensus-based distributed scheduling method for the collaborative operation of a virtual power plant in a seaport, characterized in that, Includes the following steps: S1. Construct the virtual power plant system model (SVPP) for the seaport; The virtual power plant system model of the harbor is formed by aggregating distributed energy and power equipment through power lines; The power equipment includes electrified ships, dock cranes, and refrigerated containers; The virtual power plant system model of the seaport constructed in step S1 has the following characteristics: The boundaries of a harbor extend from the traditional shore side to the shore side and ship side components; Shore power facilities establish a two-way flow channel between SVPPs and electrified vessels, allowing vessels to optimize energy dispatch based on electricity price fluctuations; The ships are all electrified and equipped with ESS (Energy Storage System), which has a dual function: it can serve as a charging load and an energy storage device. It can be regarded as a small-scale microgrid. That is, through the bidirectional coastal power system, the ships can both consume electricity and transfer the stored energy back to the shore or the power grid. An intermittent power supply strategy is adopted to regulate the storage temperature of refrigerated containers, thereby limiting the peak power consumption of refrigerated containers; This ensures that the refrigerated container operates within the permissible temperature control range. Once the temperature reaches the upper limit threshold, the cooling system will be activated to reduce the storage temperature to a safe storage range, thus saving energy. Electricity is purchased from the main grid at market prices, and electricity consumption is shifted to off-peak periods, thereby reducing consumption during peak periods; renewable energy sources are introduced, which come from photovoltaic power generation and wind power distributed generation units; Build onshore energy storage systems; S2. Construct an energy service model; By leveraging the electricity price fluctuations between seaports and the main power grid, SVPP maximizes profits and minimizes costs through energy trading, thereby promoting specific charging needs of ships and reducing ship energy expenditures. The energy service model includes S2G services, V2S and V2V energy trading; S2G service is a time-decoupling service that promotes decarbonization and allows ships to sell energy back to the main grid, using energy from ESS and other sources to increase profits and reduce costs. Preventing simultaneous buying and selling of electricity from the main grid, thereby avoiding ineffective transmission; V2S energy trading refers to ships providing energy services to port facilities; V2V energy trading refers to the ability of moored ships to exchange energy with other ships, thereby reducing the demand on the main power grid; S3. Design a distributed scheduling method for a virtual power plant in a seaport; S3.1 Framework for Distributed Scheduling; S3.2 Design a consensus-based decentralized energy management algorithm.

2. The consensus-based distributed scheduling method for collaborative operation of a virtual power plant in a seaport, as described in claim 1, is characterized in that: The onshore energy storage system stores energy during periods of low electricity prices and releases energy during periods of high electricity prices.

3. The consensus-based distributed scheduling method for collaborative operation of a virtual power plant in a seaport, as described in claim 1, is characterized in that: The decentralized scheduling framework includes entity initialization, local optimization of entities, and information exchange between entities. Entity initialization: In SVPP, set the initial parameters for each entity, including the efficiency and boundary of onshore energy storage and energy conversion equipment, power 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 facilitates a parallel and independent optimization process; Information exchange between entities: Supports collaboration across multiple entities while protecting privacy.

4. The consensus-based distributed scheduling method for collaborative operation of a virtual power plant in a seaport, as described in claim 1, is characterized in that: Step S3.2 includes the following sub-steps: Treating the entire coastline and each ship as autonomous entities, SVPP is defined as consisting of V+1 entities, denoted as n∈N = {1,2,……,N}. The V2V transactions between ships are extended to energy transactions between N entities, including the coastline, thus realizing the power balance constraint in the equation. The calculation expression is: ； In the formula, For entities n In time period t From the entity m The purchased energy is identified as a coupling variable. This indicates the demand for dock cranes in response to demand; This indicates the power consumption after using intermittent power supply; Indicates the power of the main power grid; This represents the power supplied by renewable energy sources during the time period t; This represents the charging power at time t; This represents the discharge power at time t; This indicates the amount of electricity sold by SVPP to the main grid; Transaction costs between entities are defined as: ； in, It is a fixed transaction price; The operational optimization problem of SVPP can be viewed as minimizing the operating cost and transaction cost of each entity, as follows: ； In SVPP, consensus-based ADMM is used for decentralized energy management by introducing consensus variables. and dual variables Each entity only needs to reach a consensus with its neighboring entities to guarantee entity privacy. The augmented Lagrangian function for the optimization problem is denoted as... Consensus variables should meet the following constraints for energy trading: ； The optimal solution for the consensus variable is as follows: ； In the formula, positive parameters To augment the step size of the Lagrange function.

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