Auxiliary service peak regulation method and system for distributed power supply to participate in virtual power plant

Abstract

The invention relates to the technical field of power system energy scheduling, and discloses an auxiliary service peak regulation method and system for a distributed power supply to participate in a virtual power plant. According to the method, a peak regulation mechanism in which a distributed power supply participates in an auxiliary service is established by considering a renewable energy consumption problem and participating in the auxiliary service in a competitive manner according to the overall operation cost of a peak regulation system, and in the peak period of power consumption, the whole consumption of distributed renewable energy is ensured, and the power consumption efficiency is improved. The peak regulation unit is dispatched in a free competition mode in the electricity consumption trough period, so that the operation cost of the peak regulation system is reduced, and the damage to the service life of a conventional unit is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy dispatching, and in particular relates to a method and system for peak-shaving of auxiliary services of distributed power sources participating in a virtual power plant. Background Art

[0002] In recent years, a large number of renewable energy sources have been connected to the grid. Their anti-peaking and volatility characteristics make it difficult for conventional units to provide auxiliary services such as peaking. my country has vigorously promoted the development of renewable energy and ensured the full acquisition of renewable energy, which has greatly increased the operating costs of the peaking system. With the increasing maturity of virtual power plant technology, the continuous enrichment of energy storage equipment and demand-side resources, and the explosive growth of distributed renewable energy, it is feasible and significant for these distributed power sources to participate in the peaking auxiliary services of virtual power plants. At present, when distributed power sources participate in the peaking auxiliary services of virtual power plants, there is a problem of increasing the peaking pressure of conventional units when considering the participation of distributed power sources in the operation of the power grid. Summary of the invention

[0003] In order to overcome the deficiencies and defects mentioned in the above background technology, the present application provides a method and system for peak-shaving of auxiliary services of distributed power sources participating in a virtual power plant.

[0004] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0005] In a first aspect, the present application provides a method for peak load regulation of ancillary services of a virtual power plant in which a distributed power source participates, comprising:

[0006] S1: Construct the objective function of the operating cost of the virtual power plant peak-shaving system;

[0007] S2: Construct constraints;

[0008] S3: solving the objective function based on the constraint conditions by using a particle swarm optimization method based on chaos mapping;

[0009] S4: Perform peak shaving according to the solution results.

[0010] In a second aspect, the present application provides an auxiliary service peak-shaving system for distributed power sources participating in a virtual power plant, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] The distributed power sources participating in the auxiliary service peak-shaving method of the virtual power plant provided in the present application aims at the overall operating cost of the peak-shaving system, takes into account the problem of renewable energy consumption and the form of participating in auxiliary services in a competitive manner, and establishes a peak-shaving mechanism for distributed power sources to participate in auxiliary services. During the peak power consumption period, the full consumption of distributed renewable energy is guaranteed. During the low power consumption period, the peak-shaving units should be dispatched in a free competition manner, thereby reducing the operating cost of the peak-shaving system and reducing the damage to the life of conventional units.

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 This is a flow chart of a distributed power source participating in the auxiliary service peak load regulation of a virtual power plant provided by this application;

[0015] Figure 2 This is the calculation process of the particle swarm optimization method based on chaos mapping provided by this application. DETAILED DESCRIPTION

[0016] In order to facilitate the understanding of the present invention, the present invention will be described more comprehensively and meticulously below in conjunction with the accompanying drawings and preferred embodiments of the present invention, but the protection scope of the present invention is not limited to the following specific embodiments.

[0017] Unless otherwise defined, all professional terms used below have the same meanings as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention.

[0018] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate quantity restrictions, but indicate the existence of at least one. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0019] It should be understood that in view of the participation of a large number of distributed power sources in the peak-shaving auxiliary service mechanism, considering the participation of distributed power sources in the operation of the power grid and the increase in the peak-shaving pressure of conventional units, this application proposes that distributed power sources participate in the peak-shaving of the power market of virtual power plants and establishes a peak-shaving model for distributed power sources to participate in auxiliary services.

[0020] See also Figure 1 The present application provides a method for peak-shaving of auxiliary services of a distributed power source participating in a virtual power plant, comprising:

[0021] S1: Construct the objective function of the operating cost of the virtual power plant peak-shaving system;

[0022] S2: Construct constraints;

[0023] S3: solving the objective function based on the constraint conditions by using a particle swarm optimization method based on chaos mapping;

[0024] S4: Perform peak shaving according to the solution results.

[0025] The above-mentioned distributed power sources participate in the auxiliary service peak-shaving method of the virtual power plant, aiming at the overall operating cost of the peak-shaving system, taking into account the problem of renewable energy consumption and the form of participating in auxiliary services in a competitive manner, and establish a peak-shaving mechanism for distributed power sources to participate in auxiliary services. During peak electricity consumption periods, the full consumption of distributed renewable energy is guaranteed. During low electricity consumption periods, the peak-shaving units should be dispatched in a free competitive manner, thereby reducing the operating cost of the peak-shaving system and reducing damage to the life of conventional units.

[0026] Below, with a complete example, the steps of the above-mentioned distributed power generation participating in the auxiliary service peak-shaving method of the virtual power plant are described in detail:

[0027] First, the objective function of the operating cost of the virtual power plant peak load regulation system is constructed:

[0028] When distributed power sources participate in the peak load regulation of virtual power plants, the objective is to minimize the cost and construct the operating cost objective function F of the virtual power plant peak load regulation system. min , F min The operating costs of a virtual power plant mainly include the operating costs of conventional gas turbines (CGT), energy storage systems (ESS), wind and solar power, and controllable load (CL), where controllable load consists of interruptible load (IL) and non-interruptible load. Therefore, the operating costs of a virtual power plant can be expressed as follows:

[0029]

[0030] in is the output of the nth gas turbine in period t; is the start-up and shutdown cost of the nth gas turbine in period t; is the charging and discharging power of the energy storage system in period t, where positive represents charging and negative represents discharging; is the actual output of the kth wind turbine in period t; is the actual output of the j-th PV unit in period t; is the output of the interruptible load in period t; is the non-interruptible load cost.

[0031] Specifically, the energy consumption cost of gas turbine is as follows:

[0032]

[0033] where x cGT ,y cGT 、z CGT is the operating cost coefficient of the gas unit.

[0034] The gas turbine startup and shutdown costs are as follows:

[0035]

[0036]

[0037] in, is the operating status of the nth gas generator set in period t; is the hot start cost of the nth gas unit; is the cold start cost of the nth gas generator set; The shortest permissible downtime of the nth gas generator set; The continuous downtime of the nth gas generator set; is the cold start time of the nth gas generator set.

[0038] The operating costs of the energy storage system are as follows:

[0039]

[0040] where x ESS ,y ESS 、z ESS is the charging and discharging cost coefficient of the energy storage system.

[0041] The operation and maintenance costs of wind turbines are as follows:

[0042]

[0043] where x WIND is the wind turbine maintenance cost coefficient.

[0044] The operation and maintenance costs of photovoltaic units are as follows:

[0045]

[0046] where x SUN is the maintenance cost coefficient of the PV unit.

[0047] The cost of interruptible load is as follows:

[0048]

[0049] where x IL ,y IL 、z IL The compensation cost factor obtained for the interruptible load.

[0050] Non-interruptible loads are incentive-based demand responses, provided by demand response providers, and can participate in reserve service scheduling and cooperate with virtual power plants to generate electricity and connect to the grid. The cost of non-interruptible loads is as follows:

[0051]

[0052] in is the backup cost of the z-th provider in period t, is the backup output of the zth provider in period t.

[0053] Then, construct the constraints:

[0054] System power balance constraints:

[0055]

[0056] in, is the load of the virtual power plant during period t; Q t is the demand load during period t.

[0057] Gas turbine output and climbing constraints:

[0058]

[0059] in is the lower limit of the output of the nth gas generator set; The output upper limit of the nth gas generator set; is the lower limit of the ramp rate of the nth gas generator set; It is the upper limit of the ramp rate of the nth gas generator set.

[0060] In the energy storage system, the charge and discharge rate and state of charge have a great impact on the energy storage life. The charge and discharge constraints of the energy storage system are as follows:

[0061]

[0062] Where E is the capacity of the energy storage system, in MW·h; is the state of charge of the energy storage system in period t, indicating the percentage of remaining power; Indicates the minimum value of the energy storage charge state; Indicates the maximum value of the energy storage charge state, generally 90%, 10%; The charging power of the energy storage system during period t; is the discharge power of the energy storage system during period t; The charging status of the energy storage system; It is the discharge status bit of the energy storage system, which is a 0-1 variable and the statuses are mutually exclusive. is the minimum charging and discharging power of the energy storage system, is the maximum charging and discharging power of the energy storage system.

[0063] The interruptible load call constraints are as follows:

[0064]

[0065]

[0066] in is the interruptible load power required to be called; θ max is the maximum call rate of interruptible load in a single period, is the maximum value of the sum of the continuous call rates of the interruptible load, is the output of the interruptible load in period t, It is the maximum value of the continuous call rate and interruptible load.

[0067] The non-interruptible load constraints are as follows:

[0068]

[0069] in is the lower limit of the load reduction that the mth provider can accept in a single period; is the upper limit of the load reduction that the mth provider can accept in a single period.

[0070] For further information, see Figure 2 , the particle swarm optimization based on chaos mapping is used to solve:

[0071] The particle swarm algorithm solves the problem step by step in iteration. Compared with other algorithms, the solution steps are simple and the parameter settings are few. In the whole process of change, it only changes with the global optimal solution, and does not require gradient information. It is highly intelligent. The particle swarm algorithm was proposed based on the foraging activities of bird flocks. Birds are like particles. Assuming that the particle swarm moves in a D-dimensional space, there are N particles in the particle swarm, which means that one iteration will produce N results, and each particle is D-dimensional, so there are many possibilities for one iteration of the particle swarm. In order to verify whether the particle swarm has found the optimal solution, a fitness function is usually specified to measure it. The particle swarm continuously updates and iterates under constraints, constantly compares its own optimal solution with the global optimal solution, and gradually moves closer to the global optimal solution. Assume that the position and velocity of a particle m at time t can be expressed as:

[0072] V m (t)=(V 1 ,V 2 ,…,V D )

[0073] L m (t)=(L 1 ,L 2 ,…,L D )

[0074] Because the particle swarm algorithm is a swarm intelligence algorithm, the algorithm has judgment. The speed and position update formula of the particle is as follows:

[0075] V m (t+1)=ω×V m (t)+C 1 ×rand 1 (ρb m (t)-ρre m (t))+C 2 ×rand 2 (gb m (t)

[0076] -ρre m (t))

[0077] L m (t+1)=L m (t)+V m (t+1)

[0078] Where ω is the inertia weight factor, a non-negative number, C 1 and C 2 is the acceleration constant, rand 1 and rand 2 Indicates generating a random number in the range of 0 to 1, gb m (t) represents the best historical position of the entire group, ρrem (t) represents the best historical position of a single particle;

[0079] The quality of the initial particle swarm greatly affects the performance of the algorithm, not only related to the convergence speed of the algorithm, but also the quality of the final solution. Randomly generating the initial population is the initialization method adopted by most algorithms. Although this method is simple and easy to operate, the initial solution generated is often unsatisfactory and of poor quality, so it takes a long time to find the optimal solution. Therefore, chaotic mapping is introduced. Chaotic mapping has the characteristics of traversal. The variables after chaotic mapping become more outstanding, which helps the particle swarm get rid of the dilemma of falling into the local optimal solution. The specific mapping formula is as follows:

[0080]

[0081] Among them, Y k represents the population ratio at time k, t is a constant that controls the movement of particles, and t is usually 4. Use the following formula to normalize it to between 0 and 1, and then perform chaos mapping.

[0082]

[0083] Next, the normalized particles are subjected to chaotic mapping to obtain particles after chaotic iteration. The obtained particles need to be transformed into the original decision variable space using the following formula.

[0084]

[0085] In the formula, y min,j Represents the minimum value in the particle, y max,j represents the maximum value in the particle, c is a constant, is the k+1th particle.

[0086] It is worth pointing out that chaos mapping is a special treatment of particles in the particle swarm to prevent the above particle swarm algorithm from falling into a local optimal solution. In this application, a particle swarm optimization method based on chaos mapping is used for solving, which can improve the solution accuracy. Specifically, the input of the particle swarm optimization method based on chaos mapping is the objective function and constraints, and the algorithm automatically outputs the solution of the objective function after processing.

[0087] The present application also provides a distributed power source participating in an auxiliary service peak-shaving system for a virtual power plant, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program. The distributed power source participating in an auxiliary service peak-shaving system for a virtual power plant can implement various embodiments of the above-mentioned distributed power source participating in an auxiliary service peak-shaving method for a virtual power plant, and can achieve the same beneficial effects, which will not be described in detail here.

[0088] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for peak load regulation of auxiliary services of distributed power sources participating in a virtual power plant, characterized in that: include: S1: Construct the objective function of the operating cost of the virtual power plant peak-shaving system; S2: Construct constraints; S3: solving the objective function based on the constraint conditions by using a particle swarm optimization method based on chaos mapping; S4: Perform peak shaving according to the solution results.

2. The method for peak load regulation of distributed power sources participating in ancillary services of a virtual power plant according to claim 1, characterized in that: The S1 includes: The objective function of the operating cost of the virtual power plant peak-shaving system is constructed based on the gas turbine operating cost, energy storage system operating cost, wind and solar operating cost, and controllable load cost.

3. The method for peak load regulation of distributed power sources participating in an auxiliary service of a virtual power plant according to any one of claims 1 or 2, characterized in that: The objective function satisfies the following relationship: In the formula, represents the gas turbine energy cost, represents the gas turbine startup and shutdown cost, represents the operating cost of the energy storage system, represents the operation and maintenance cost of wind turbines, represents the operation and maintenance cost of the photovoltaic unit, represents the cost of interruptible load, represents the cost of the non-interruptible load, is the output of the nth gas turbine in period t; is the start-up and shutdown cost of the nth gas turbine in period t; is the charging and discharging power of the energy storage system in period t, where positive represents charging and negative represents discharging; is the actual output of the kth wind turbine in period t; is the actual output of the j-th PV unit in period t; is the output of the interruptible load in period t; is the non-interruptible load cost.

4. The method for peak load regulation of distributed power sources participating in ancillary services of a virtual power plant according to claim 1, characterized in that: The constraints include: system power balance constraints, gas turbine output and ramp constraints, energy storage system charging and discharging constraints, interruptible load call constraints, and non-interruptible load constraints.

5. The method for peak load regulation of distributed power sources participating in ancillary services of a virtual power plant according to claim 1, characterized in that: The system power balance constraint satisfies the following relationship: in, is the output of the nth gas turbine in period t, is the actual output of the kth wind turbine in period t, is the actual output of the j-th photovoltaic unit in period t, is the charging and discharging power of the energy storage system in period t, is the output of the interruptible load in period t, is the load of the virtual power plant during period t; Q t is the demand load during period t, is the reserve output of the zth provider in period t; The gas turbine output and climbing constraints satisfy the following relationship: in, is the lower limit of the output of the nth gas generator set; The output upper limit of the nth gas generator set; is the lower limit of the ramp rate of the nth gas generator set; is the upper limit of the ramp rate of the nth gas generator set; The charging and discharging constraints of the energy storage system satisfy the following relationship: Where E is the capacity of the energy storage system, in MW·h; is the state of charge of the energy storage system in period t, indicating the percentage of remaining power; Indicates the minimum value of the energy storage charge state; Indicates the maximum value of the energy storage charge state, The charging power of the energy storage system during period t; is the discharge power of the energy storage system during period t; is the charging status of the energy storage system, is the discharge status of the energy storage system, is the minimum charging and discharging power of the energy storage system, is the maximum charging and discharging power of the energy storage system; Interruptible load call constraints: in, is the interruptible load power required to be called; θ max is the maximum call rate of interruptible load in a single period, is the maximum value of the sum of the continuous call rates of the interruptible load, is the output of the interruptible load in period t, is the maximum value of the sum of the continuous call rates of the interruptible load; The non-interruptible load constraint satisfies the following relationship: in, is the lower limit of the load reduction that the mth provider can accept in a single period; is the upper limit of the load reduction that the mth provider can accept in a single period.

6. The method for peak load regulation of distributed power sources participating in ancillary services of a virtual power plant according to claim 1, characterized in that: The S3 includes: The particle swarm optimization method based on chaos mapping is used to continuously update and iterate under constraints to solve the objective function: Assume that the position and velocity of a particle m at time t are expressed as: V m (t)=(V1,V2,…,V D ) L m (t)=(L1,L2,…,L D ) The particle velocity and position update formulas are as follows: V m (t+1)=ω×V m (t)+C1×rand1(ρb m (t)-ρre m (t))+C2×rand2(gb m (t) -ρre m (t)) L m (t+1)=L m (t)+V m (t+1) Chaotic mapping is introduced, wherein the chaotic mapping in the particle swarm optimization method based on chaotic mapping satisfies the following relationship: Among them, t is a constant used to control the movement of particles, Y k represents the population proportion at time k, Use the following formula to normalize it to between 0 and 1, and then perform chaos mapping; The normalized particles are subjected to chaotic mapping to obtain the particles after chaotic iteration, and the obtained particles are transformed into the original decision variable space using the following formula: In the formula, y min,j Represents the minimum value in the particle, y max,j represents the maximum value in the particle, c is a constant, is the k+1th particle.

7. A distributed power source participating in an auxiliary service peak-shaving system of a virtual power plant, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 6 are implemented.

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