Virtual power plant price incentive method giving consideration to multiple subjects
By establishing a two-layer optimization model for virtual power plants and flexible resources, the problems of collaborative operation within virtual power plants and flexible resources participating in scheduling are solved, and the electricity price incentive mechanism is realized, which increases operating profits and reduces operating costs.
Patent Information
- Application Number
- CN202311574908.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-23
- Publication Date
- 2025-03-21
AI Technical Summary
The existing virtual power plant price incentive method cannot achieve collaborative operation between different entities within the virtual power plant, nor can it incentivize flexible resources to spontaneously and actively participate in the acceptance of superior dispatch instructions.
By establishing a virtual power plant optimization layer model and a flexibility resource optimization layer model, combining the operational objective optimization constraints, a planning problem model is formed, and the virtual power plant pricing of each flexible resource by solving the model.
The coordinated operation between different entities within the virtual power plant is realized. By setting reasonable electricity price incentive flexibility resources, it spontaneously and actively participates in accepting superior dispatch instructions, the operating profit of the virtual power plant is increased and the total operating cost of the system is reduced.
Smart Images
Figure CN119671602A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a price evaluation method for flexibility resources of a virtual power plant. Background Art
[0002] In order to promote the participation of diversified and massive flexible resources in grid dispatching and market transactions, virtual power plants (VPPs) have emerged as an effective means to achieve efficient management of large-scale flexible resources. Compared with physical generators, virtual power plants connect flexible resources and power systems at the same time. Through the coordinated management of internal photovoltaic, energy storage, charging piles, adjustable loads and other equipment, they achieve resource integration and allocation, explore and utilize the respective advantages of different types of equipment, and provide a variety of services such as energy balance, voltage support, frequency regulation, and congestion management. They have shown great economic value and development potential. As a smart energy management system, virtual power plants can comprehensively balance power supply, coordinate and optimize grid stability, and effectively solve the impact of new energy power generation on the grid.
[0003] However, the existing price incentive methods for virtual power plants cannot achieve coordinated operations among different entities within the virtual power plant, nor can they motivate flexible resources to spontaneously and actively participate in accepting superior dispatch instructions. Summary of the invention
[0004] The purpose of the present invention is to overcome the problem that the existing virtual power plant price incentive method cannot achieve coordinated operation among different entities within the virtual power plant, and cannot motivate flexible resources to spontaneously and actively participate in accepting superior scheduling instructions, and provide a virtual power plant price incentive method that takes multiple entities into consideration.
[0005] The present invention provides a virtual power plant price incentive method taking into account multiple subjects, and the specific steps are as follows:
[0006] Step 1: Establish a virtual power plant optimization layer model and a flexibility resource optimization layer model;
[0007] The virtual power plant optimization layer model includes the virtual power plant operating cost function and operating cost optimization constraints;
[0008] The flexibility resource optimization layer model includes the operation objective function and the operation objective optimization constraints;
[0009] Step 2: Substitute the operation target optimization constraints into the operation cost optimization constraints to obtain the planning problem model;
[0010] Step 3: Solve the planning problem model and obtain the pricing of each flexibility resource by the virtual power plant.
[0011] Furthermore, the virtual power plant operating cost function in step 1 is:
[0012]
[0013] Among them, ΔT is the duration of the optimization period interval; T is the total number of virtual power plant operation periods; t is the time;
[0014] F i DG (·) is the cost function of the generator set as follows:
[0015]
[0016]
[0017] in, is the active power of generator set i at time t; and
[0018] Furthermore, the operating cost optimization constraints in step 1 include:
[0019] System power flow equation:
[0020]
[0021]
[0022] Among them, P i , Q i Represent the active and reactive injection power of the node respectively; r ij and x ij are the resistance and reactance between line i and line j respectively; θ i , V i , P i , Q i are the phase, voltage amplitude, injected active power and injected reactive power at node i respectively; N B is the total number of nodes in the virtual power plant;
[0023] The active power and reactive power of each node must meet the following balance conditions:
[0024]
[0025]
[0026] in, is the actual power consumption of aggregator k at time t;
[0027] Safety limits of power flow and voltage in each branch of the system:
[0028]
[0029] V i,min ≤V i ≤V i,max
[0030] Among them, S ij,N is the rated capacity of the transmission line between line i and line j; V i,max and V i,min are the upper and lower limits of node voltage amplitude respectively; the flow calculation of each branch is as follows:
[0031]
[0032]
[0033] Power range of generator sets and renewable energy sources:
[0034]
[0035]
[0036]
[0037] in, is a discrete decision variable; is the rated capacity of generator set i; the output range of renewable energy is as follows:
[0038]
[0039] Wherein, η is the power energy conversion coefficient;
[0040] Model constraints for energy storage devices:
[0041]
[0042]
[0043]
[0044]
[0045] Price range constraints:
[0046]
[0047]
[0048] Furthermore, the operation objective function in step 1 is:
[0049]
[0050] in,
[0051] The effect function representing the demand-side response load is:
[0052] and
[0053] in, Furthermore, the operation target optimization constraints in step 1 include:
[0054] Power balance constraints:
[0055]
[0056]
[0057] Constraints on renewable energy and generator output model:
[0058]
[0059]
[0060]
[0061]
[0062]
[0063] Internal demand response load model constraints:
[0064]
[0065]
[0066]
[0067] Flexibility within the resource aggregator model constraints:
[0068]
[0069]
[0070]
[0071] Power interaction limits with the upper-level virtual power plant:
[0072]
[0073] Furthermore, the specific process of step 2 is as follows:
[0074] Step 21: Convert the operation target optimization constraint condition into the KKT optimization condition, and substitute it into the operation cost optimization constraint condition to obtain the combined constraint condition;
[0075] Step 2: Use the big M method to convert the bilinear constraints in the combined constraints into a mixed integer linear constraint model as the planning problem model.
[0076] Furthermore, flexibility resources include distributed photovoltaic power generation equipment, distributed wind power equipment, distributed energy storage equipment, distributed cogeneration equipment, electric vehicle charging equipment and gas generator sets.
[0077] The beneficial effects of the present invention are:
[0078] The present invention provides a virtual power plant price incentive method that takes multiple subjects into consideration, which can realize the coordinated operation among different subjects within the virtual power plant. By setting reasonable electricity prices, flexible resources are encouraged to spontaneously and actively participate in accepting superior dispatch instructions. As the proportion of price incentive resources increases, the operating profit of the virtual power plant continues to increase, and the total operating cost of the system is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS
[0079] Figure 1 The schematic diagram of the price incentive model of virtual power plant for flexibility resources; the price incentive model of virtual power plant for flexibility resources includes virtual power plant optimization layer model and flexibility resource optimization layer model;
[0080] Figure 2 Schematic diagram of the transformation and solution method of the virtual power plant optimization layer model and the flexibility resource optimization layer model into the planning problem model. DETAILED DESCRIPTION
[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0082] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.
[0083] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.
[0085] like Figure 1As shown, the price incentive of the virtual power plant for internal resources is based on the following two-layer optimization model, in which the upper layer is used for virtual power plant operation optimization, and the lower layer is used for benefit optimization of flexibility resource aggregators. The upper layer provides guidance on power generation prices for the lower layer, and the lower layer provides feedback on demand information to the upper layer.
[0086] (1) Virtual Power Plant Optimization Layer
[0087] The cost function of virtual power plant operation is defined as follows, which includes the cost of purchasing electricity from the upper power grid, the power generation cost of the internal controllable units, and the revenue from selling electricity to the lower-level flexibility resources:
[0088]
[0089] Where: F i DG (·) is the cost function of the generator set as follows:
[0090]
[0091]
[0092] Where:
[0093] The constraints considered in the virtual power plant operation optimization are as follows:
[0094] 1) System power flow equation:
[0095]
[0096]
[0097] Where: P i ,Q i Represent the active and reactive injected power of the node respectively.
[0098] The active power and reactive power of each node must meet the following balance conditions:
[0099]
[0100]
[0101] 2) The power flow and voltage safety limits of each branch of the system are as follows:
[0102]
[0103] V i,min ≤V i ≤V i,max (9)
[0104] The calculation method of each branch flow is as follows:
[0105]
[0106]
[0107] 3) The power ranges of traditional generators and renewable energy are as follows:
[0108]
[0109]
[0110]
[0111] Based on opportunity constraints and considering the uncertainty of new energy, the output range of renewable energy can be obtained as follows:
[0112]
[0113] 4) The model constraints of energy storage equipment are as follows:
[0114]
[0115]
[0116]
[0117]
[0118] 5) The price range constraints are as follows: Since the price set by the virtual power plant for the underlying flexibility resources should be within a certain range, the prices of various power products should meet the following constraints:
[0119]
[0120]
[0121] The decision variables of the optimization model include:
[0122] (2) Flexible resource optimization layer
[0123] After the virtual power plant receives the power price information, the flexibility resource aggregator should formulate its own operation plan according to the power price curve. Its operation goal is to maximize its own interests. The objective function is defined as follows, including the power supply income of internal resources, the power generation cost of internal units, and the product purchase cost from the upper-level virtual power plant:
[0124]
[0125] Where: The effect function representing the demand-side response load is defined as follows:
[0126]
[0127] Where:
[0128] 1) Power balance constraints:
[0129]
[0130]
[0131] 2) Constraints on renewable energy and generator output models:
[0132]
[0133]
[0134]
[0135]
[0136]
[0137] 3) Internal demand-side response load model constraints:
[0138]
[0139]
[0140]
[0141] 4) Internal flexibility Resource aggregator model constraints:
[0142]
[0143]
[0144]
[0145] 5) Power interaction limit with the upper virtual power plant:
[0146]
[0147] The decision variables for flexibility resource aggregators include:
[0148] (3) Model processing and solution method:
[0149] The transformation and solution method of the proposed model are as follows Figure 2 As shown, it can be divided into three steps as a whole:
[0150] 1. First, convert the lower-level optimization problem into KKT optimization conditions and substitute them into the upper-level constraints;
[0151] 2. Use the big M method to convert the bilinear constraints in the constraints into mixed integer linear constraints;
[0152] 3. Use software such as Cplex to solve the final mixed integer linear programming problem.
[0153] The benefits of virtual power plant agents and power market operators under the virtual power plant price incentive model are simulated. The simulation results show that under this model, collaborative operation among different entities within the virtual power plant can be achieved, and flexible resources can be encouraged to spontaneously and actively participate in accepting superior dispatch instructions by setting reasonable electricity prices; with the increase in the proportion of price-incentive resources, the operating profit of the virtual power plant continues to increase, and the total operating cost of the system is effectively reduced.
[0154] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the features of the various dependent claims and herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with the individual embodiments may be used in other embodiments.
Claims
1. A virtual power plant price incentive method taking into account multiple subjects, characterized in that: The specific steps are as follows: Step 1: Establish a virtual power plant optimization layer model and a flexibility resource optimization layer model; The virtual power plant optimization layer model includes a virtual power plant operating cost function and operating cost optimization constraints; The flexibility resource optimization layer model includes an operation objective function and operation objective optimization constraints; Step 2: Substitute the operation target optimization constraints into the operation cost optimization constraints to obtain the planning problem model; Step three: Solve the planning problem model to obtain the pricing of each flexibility resource by the virtual power plant.
2. A virtual power plant price incentive method taking into account multiple subjects according to claim 1, characterized in that: The operating cost function of the virtual power plant in step 1 is: Among them, ΔT is the duration of the optimization period interval; T is the total number of virtual power plant operation periods; t is the time; F i DG (·) is the cost function of the generator set as follows: in, is the active power of generator set i at time t; and 3. A virtual power plant price incentive method taking into account multiple subjects according to claim 2, characterized in that: The operating cost optimization constraints in step 1 include: System power flow equation: Among them, P i , Q i Represent the active and reactive injection power of the node respectively; r ij and x ij are the resistance and reactance between line i and line j respectively; θ i , V i , P i , Q i are the phase, voltage amplitude, injected active power and injected reactive power at node i respectively; N B is the total number of nodes in the virtual power plant; The active power and reactive power of each node must meet the following balance conditions: in, is the actual power consumption of aggregator k at time t; Safety limits of power flow and voltage in each branch of the system: In i,min ≤V i ≤V i,max Among them, S ij,N is the rated capacity of the transmission line between line i and line j; V i,max and V i,min are the upper and lower limits of node voltage amplitude respectively; the flow calculation of each branch is as follows: Power range of generator sets and renewable energy sources: in, is a discrete decision variable; is the rated capacity of generator set i; The output range of renewable energy is as follows: Wherein, η is the power energy conversion coefficient; Model constraints for energy storage devices: Price range constraints:
4. A virtual power plant price incentive method taking into account multiple subjects according to claim 1, 2 or 3, characterized in that: The operational objective function in step 1 is: in, The effect function representing the demand-side response load is: and in, 5. A virtual power plant price incentive method taking into account multiple subjects according to claim 4, characterized in that: The operational goal optimization constraints in step 1 include: Power balance constraints: Constraints on renewable energy and generator output model: Internal demand response load model constraints: Flexibility within the resource aggregator model constraints: Power interaction limits with the upper-level virtual power plant:
6. A virtual power plant price incentive method taking into account multiple subjects according to claim 1, 2, 3 or 5, characterized in that: The specific process of step 2 is as follows: Step 21: Convert the operation target optimization constraint condition into the KKT optimization condition, and substitute it into the operation cost optimization constraint condition to obtain the combined constraint condition; Step 2: Use the big M method to convert the bilinear constraints in the combined constraints into a mixed integer linear constraint model as the planning problem model.
7. A virtual power plant price incentive method taking into account multiple subjects according to claim 6, characterized in that: Flexibility resources include distributed photovoltaic power generation equipment, distributed wind power equipment, distributed energy storage equipment, distributed cogeneration equipment, electric vehicle charging equipment and gas-fired generators.