Coal power plant optimization scheduling system matched with virtual power plant

Abstract

The invention discloses a coal power plant optimization scheduling system matched with a virtual power plant. Belongs to the technical field of virtual power plant optimization scheduling, and discloses a virtual power plant optimization scheduling system which is used for aggregating a coal power plant and other distributed energy sources to form a collaborative scheduling and management platform, realizing power supply collaborative optimization scheduling of the virtual power plant to the coal power plant, and optimizing operation of the coal power plant through the virtual power plant optimization scheduling system. Interactive fusion of the virtual power plant and the coal power plant is realized; the operation state of the coal power plant is monitored in real time through the intelligent terminal, the power grid load is balanced through the energy storage system, the coal power plant and other energy generating capacity are intelligently dispatched according to the power grid requirement and the real-time state of each energy source, and the power generation plan and the operation strategy of the coal power plant are optimized by predicting the load and the energy source price through prediction and intelligent optimization algorithms. Finally, the virtual power plant participates in the power market transaction on behalf of the coal power plant, thereby improving the operation efficiency of the coal power plant, and enhancing the reliability of the whole power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plant optimization and scheduling, and relates to a coal power plant resource optimization and scheduling system; specifically, it relates to a coal power plant optimization and scheduling system that cooperates with a virtual power plant. Background Art

[0002] As an important part of traditional energy, coal-fired power plants face a series of challenges and problems. The traditional energy structure dominated by coal-fired power is facing transformation pressure. First, as one of the main sources of carbon emissions, coal-fired power plants need to reduce carbon emissions through technologies such as CCUS, while renewable energy sources such as wind and solar energy have obvious volatility and intermittency. These uncertainties have brought challenges to the stability of the power grid. Secondly, combined with the current status of the electricity market, the market share of coal-fired power as a traditional energy source has been impacted, and users have diversified their demand for electricity. Coal-fired power plants need to adjust their operating strategies and resource scheduling to adapt to market changes and thus improve their operating efficiency and market competitiveness. Although coal-fired power technology continues to advance, there are still technical bottlenecks in improving energy efficiency and reducing emissions. Coal-fired power companies face operating pressures such as rising fuel costs and increased environmental protection investment, and their economic benefits are challenged. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to propose a collaborative optimization scheduling system that can optimize the resource utilization of coal-fired power plants, improve operating efficiency, obtain maximum benefits of coal-fired power plants, and reasonably integrate virtual power plants.

[0004] The technical solution of the present invention is: a coal power plant optimization dispatching system cooperating with a virtual power plant according to the present invention, wherein the system is coordinated by a virtual power plant system to optimize the dispatching of coal power plant resources, including a virtual power plant system and a coal power plant;

[0005] The virtual power plant system includes distributed energy, energy storage system, controllable flexible load, virtual power plant management platform, power demand side management and power market.

[0006] Furthermore, the optimization dispatching system aggregates coal-fired power plants and distributed energy resources to form a unified dispatching and management platform; then the integrated energy storage system balances the power generation output of the coal-fired power plant. When the power demand is low, the energy storage system stores excess electricity and releases energy during peak hours, thereby smoothing the power generation curve of the coal-fired power plant, reducing the frequency of rapid start and stop, and improving operational flexibility; then, based on the controllable flexible load information provided by the virtual power plant and the grid load balanced by demand-side management, the coal-fired power plant optimizes its own power generation plan and fuel usage, reducing the number of starts and stops and operating costs.

[0007] Furthermore, the virtual power plant management platform includes an energy management system, prediction technology and optimization and control technology;

[0008] The virtual power plant management platform combines intelligent terminals to aggregate data, demand response side and power market transaction side. First, the energy management system is responsible for data collection, monitoring and issuing control instructions through the Internet of Things, cloud computing and big data analysis technology to ensure real-time transmission of information and real-time monitoring of the operating status of coal-fired power plants; secondly, by collecting data, load forecasting and energy price forecasting, the power generation plan and operation strategy of the coal-fired power plant are optimized; finally, the power generation of the coal-fired power plant and other energy sources are intelligently dispatched according to the demand of the power grid and the real-time status of each energy source.

[0009] Furthermore, the power demand side management center is a demand response and intelligent dispatching center, which interacts with the power market and controllable loads to implement demand side management;

[0010] Flexible load management can be achieved by using incentives or price signals to encourage users to reduce electricity consumption when electricity prices are high or increase electricity consumption when electricity prices are low.

[0011] Furthermore, the coal-fired power plant can reduce power generation during peak electricity demand periods and increase power generation during low electricity demand periods.

[0012] Furthermore, the system realizes the optimal optimization scheduling of coal-fired power plants through interactive integration of virtual power plant systems and coal-fired power plants to form a scheduling system; deeply and interactively cooperates with coal-fired power plants from the perspectives of energy structure, integrated energy storage, response information and electricity market to optimize the operation strategy of coal-fired power plants; and uses the chaotic quantum particle swarm optimization algorithm with adaptive crossover operator to seek the maximum net profit.

[0013] Furthermore, the specific operation steps of the chaotic quantum particle swarm optimization algorithm are as follows:

[0014] (1) Establishing the objective function: To reflect the economic efficiency of VPP, the optimization goal is to maximize the net profit of VPP in cooperation with coal-fired power plants. The decision variables include revenue and cost. The revenue refers to the revenue from electricity sales within the virtual power plant and the revenue from the sale of grid-connected electricity. The cost refers to the operating cost of the virtual power plant and coal-fired power plant as well as the reward and punishment measures. The objective function is established as follows:

[0015]

[0016] Where: The revenue from electricity sales within the VPP; F is the income from VPP grid-connected electricity sales; sc,pun Rewards and penalties for breach of contract; vpp It is the VPP operating cost;

[0017] (2) The coal-fired power plants and virtual power plants are optimally dispatched using the chaotic quantum particle swarm optimization algorithm with an adaptive crossover operator. The power generation costs of virtual power plants and coal-fired power plants, the power generation costs of new energy sources, the start-up and shutdown costs, the operating costs, the maintenance costs, and the basic parameters of the algorithm are set for the partial revenue of coal-fired power plants and the electricity market. The parameters include the population size and the maximum number of iterations. The population is randomly initialized, the fitness of the population is calculated, the maximum net profit value of the optimized scheduling system is obtained, and it is determined whether the iteration termination conditions are met. If so, the current cost is output as the maximum net profit value. Otherwise, the initialized population is divided into several ordinary sub-populations, and optimization is performed based on the individual behaviors in the initialized population.

[0018] Furthermore, the specific process of step (1) is as follows:

[0019] (1.1) The profit from selling electricity within the virtual power plant is:

[0020]

[0021] Where: is the internal electricity price of the virtual power plant at time t; The load required inside the virtual power plant; the amount of electricity that responds at any moment;

[0022] (1.2) The profit of a virtual power plant selling electricity to the power market is:

[0023]

[0024] Where: is the market electricity price at time t; is the actual power output of the market at time t;

[0025] (1.3) The rewards and penalties for breach of the virtual power plant contract are:

[0026]

[0027] Where: F sc,pun is the penalty fee for default electricity; λ is the penalty price factor for default; is the market electricity price at time t; They are the market contract power and actual power output at time t respectively;

[0028] (1.4) VPP operating costs mainly include coal-fired power plant fuel and unit operating costs, renewable energy power generation costs, coal-fired power plant start-up and shutdown costs, and maintenance costs, namely:

[0029]

[0030] Where: F dg F is the fuel unit operating cost; M For coal power plant operating costs; They are the response power and non-response power at time t respectively; They are the reward electricity price for responding load and the penalty electricity price for not responding load at time t;

[0031] Energy consumption cost of fuel unit:

[0032]

[0033] Where: P dg is the output of the fuel unit; a, b, c are the operating cost coefficients of the fuel unit;

[0034] The operating cost function of a coal-fired power plant is:

[0035]

[0036] Where: To provide power for coal-fired power plants; c is the penalty cost per unit of flue gas emission; e is the amount of flue gas generated per unit output of the coal-fired power plant.

[0037] Furthermore, the specific process of step (2) is as follows:

[0038] (2.1) Initialize the population using the Logistics chaos map; represent the various costs in the system, including the VPP system and coal-fired power plant power generation costs, renewable energy power generation costs, fuel unit costs, operating costs, maintenance costs, and power market revenues, with individual particles. Input the training set into the model for training, and use the average absolute error obtained as the fitness function. At the same time, add hybrid variables to the initial population; use the Logistics map to transform it into a chaotic sequence, and use the ergodicity of chaotic motion to search for the optimal solution of the variables to be optimized, so as to improve the effectiveness and convergence speed of the algorithm. The Logistics map is as follows:

[0039] S n+1 =aS n (1-S n )

[0040] Where: S n is the initial sequence, S n ∈[0,1], a is the Logistics parameter representing the chaos initiator, a∈[0,4].

[0041] (2.2) Evaluate the fitness based on the initialized population; calculate the fitness value, the individual's historical optimal position, the group's optimal position, and the center of gravity of all particles; the population dynamically moves in the determined search space to find the position corresponding to the optimal fitness value as the best solution;

[0042] In each iteration, the particle will track two extreme values, one is the optimal solution Pbest found by the particle itself so far, and the other is the optimal solution Gbest found by the entire population so far; its state is represented by the wave function ψ(x, t), whose square value represents the probability of x appearing at the position, and is represented by the attractor p k =(p1,p2,p3,...,p k ) is used as the center to constrain the quantum particles in the PSO algorithm, so that the net benefit can converge to its local P without explosion; the attractor p k for:

[0043] p k =ψ·P best_k +(1+ψ)G best

[0044] Where: P best_k The optimal net profit of VPP in the current iteration in conjunction with coal-fired power plants; G best The current global VPP cooperates with the coal power plant to achieve the best net profit; k is an attractor, used to update the kth net profit position;

[0045] (2.3) Improve the algorithm and optimize the search step size; update the position solution, obtain the probability density function and distribution function by solving the Schrödinger equation, and use the Monte Carlo method to obtain the position of the kth net profit. The position update is:

[0046]

[0047] Where: x k is the position of the kth net benefit, that is, the net benefit of the kth VPP in cooperation with the coal-fired power plant operation is updated; γ is an innovation parameter, which can control the convergence speed of the algorithm; M best The current average optimal solution; ψ and u are random numbers between (0,1) that follow a uniform distribution, with a probability of + or - of ±0.5 respectively;

[0048] The value of γ depends on the research scenario. It can be a fixed value or change dynamically in a certain way. The formula is as follows:

[0049]

[0050] Where: T max , K r are the maximum and current iteration times respectively; α is the power purchase decision coefficient, β is the power sales decision coefficient, and R is the step size optimization factor;

[0051] (2.4) Genetically encode the samples, introduce an adaptive crossover operator to increase the diversity of the population, improve the algorithm's ability to jump out of the local optimum, and introduce a vertical crossover operator into the quantum particle swarm algorithm. Vertical crossover is an arithmetic crossover between two different dimensions of a particle in a certain population. Normalization is used to solve the problem of different value ranges of elements in different dimensions. At the same time, each operation only generates one offspring particle and only updates one dimension.

[0052] Assuming that the d1th and d2th dimensions of the revenue update position X(j) participate in the vertical crossover, the trivial solution MS is generated according to the following formula vc (j,d1)

[0053] MS vc (j,d1)=r×X(j,d1)+(1-r)×X(j,d2)

[0054] Where: j∈N(1,M), d1,d2∈N(1,D), r∈[0,1], MS w (j, d1) is the d1-dimensional offspring generated by the vertical crossover of the d1-dimensional and d2-dimensional individual X(j); and the adaptive crossover probability improvement formula is crossovered to improve the algorithm's optimization ability and generate a new population; the adaptive crossover probability improvement formula is as follows:

[0055]

[0056] Where: p b1 and p b2 is a constant, P best M is the optimal net profit of VPP in the current iteration in conjunction with coal power plant operation. best The current average optimal solution, l' represents the fitness value corresponding to the better offspring particle after crossover operation in two different dimensions of a particle;

[0057] (2.5) Update the VPP to cooperate with the net profit of coal-fired power plant operation, introduce the spiral search strategy, improve the global search performance, calculate the fitness value of the new population, and update the fitness value; determine whether the crossover requirements are met. If they are met, perform vertical crossover, otherwise, go to the termination condition; the improved formula is as follows:

[0058]

[0059] Where, X k+1 Provide net benefits for the operation of newer VPPs in conjunction with coal-fired power plants;

[0060] (2.6) Check the termination condition; if the number of iterations is reached or the termination condition is met, terminate the iteration; otherwise, go to step 2);

[0061] (2.7) and obtain the net profit corresponding to the optimal fitness value, that is, the optimal net profit value of the coal-fired power plant under the virtual power plant optimization dispatching system.

[0062] The beneficial effects of the present invention are as follows: the present invention optimizes the operation of coal-fired power plants through a virtual power plant optimization and dispatching system, thereby realizing the interactive integration of virtual power plants and coal-fired power plants; real-time monitoring of the operating status of coal-fired power plants is performed through intelligent terminals, and the load of the power grid is balanced by energy storage systems. According to the power grid demand and the real-time status of each energy source, the power generation of coal-fired power plants and other energy sources is intelligently dispatched, and the power generation plan and operation strategy of coal-fired power plants are optimized by predicting loads and energy prices through prediction and intelligent optimization algorithms. Finally, virtual power plants are used as market participants to represent coal-fired power plants in power market transactions. Through the in-depth cooperation between virtual power plants and coal-fired power plants, not only the operating efficiency of coal-fired power plants can be improved, but also the reliability of the entire power grid can be enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0064] Figure 2 It is the algorithm flow chart of the present invention;

[0065] Figure 3 This is a distributed transaction model diagram of a virtual power plant based on blockchain technology in the present invention. DETAILED DESCRIPTION

[0066] The specific technical scheme of the present invention is further described in detail below with reference to specific examples.

[0067] like Figure 1 As shown, the present invention describes a coal power plant optimization and dispatching system that cooperates with a virtual power plant. The system is composed of a virtual power plant system that coordinates and optimizes the resource dispatching of the coal power plant. The system mainly includes a virtual power plant system and a coal power plant. Its core virtual power plant system is mainly composed of distributed energy, energy storage system, flexible controllable load, virtual power plant cloud platform, demand response and intelligent dispatching center and power market.

[0068] Furthermore, the virtual power plant optimization dispatching system aggregates coal-fired power plants and distributed energy resources such as wind power, solar energy, small hydropower and other new energy sources to form a unified dispatching and management platform; then integrates the energy storage system to balance the power generation output of the coal-fired power plant. When the power demand is low, the energy storage system stores excess electricity and releases energy during peak hours, thereby smoothing the power generation curve of the coal-fired power plant, reducing the frequency of rapid start and stop, and improving operational flexibility; then, based on the controllable flexible load information of commercial and industrial users provided by the virtual power plant and the grid load balanced by demand-side management, the coal-fired power plant optimizes its own power generation plan and fuel use, reducing the number of starts and stops and operating costs.

[0069] Furthermore, the virtual power plant cloud platform center includes an energy management system, forecasting technology, and optimization and control technology; the center combines intelligent terminals to aggregate data, demand response side, and power market trading side. First, the energy management system is responsible for data collection, monitoring, and issuing control instructions through the Internet of Things, cloud computing, and big data analysis technology to ensure real-time transmission of information and real-time monitoring of the operating status of coal-fired power plants; secondly, by collecting data, load forecasting, and energy price forecasting, the power generation plan and operation strategy of the coal-fired power plant are optimized. Finally, the power generation of the coal-fired power plant and other energy sources are intelligently dispatched according to the grid demand and the real-time status of each energy source.

[0070] Furthermore, the power demand side management center, namely demand response and intelligent scheduling, interacts with the power market and controllable loads to implement demand side management; through incentives or price signals, users are encouraged to reduce electricity consumption when electricity prices are high or increase electricity consumption when electricity prices are low, thereby achieving flexible load management; coal-fired power plants can reduce power generation during peak power demand periods and increase power generation during low demand periods to improve efficiency; and their power generation plans can be adjusted more flexibly to reduce the number of starts and stops and extend the service life of equipment.

[0071] Furthermore, virtual power plants and electricity markets are based on blockchain technology. The virtual power plant trading model based on blockchain technology first builds an open and transparent trading platform for virtual power plants and coal-fired power plants. Each power plant uploads information such as electricity prices and electricity consumption to the blockchain, which encourages power plants to reduce costs, promotes market competition, and makes resources more reasonably and fairly allocated; secondly, point-to-point transactions can be realized between distributed energy and loads within the virtual power plant. The consensus mechanism of blockchain technology ensures that information is open and transparent, and each distributed resource can achieve flexible transactions and automatic settlement through smart contracts; participate in a larger range of electricity transactions, achieve optimal resource allocation, and participate in the electricity spot market and ancillary service market in a market-oriented manner.

[0072] Furthermore, the system realizes the best optimization dispatch of coal power plant power source through interactive integration of virtual power plant and coal power plant to form a dispatch system; deeply and interactively cooperates with coal power plant from multiple aspects such as energy structure, integrated energy storage, response information, power market, etc. to optimize the operation strategy of coal power plant and maximize the profit of coal power plant; uses the chaotic quantum particle swarm optimization algorithm (ACCQPSO) of adaptive crossover operator to seek the maximum net profit, that is, the maximum profit of coal power plant;

[0073] Furthermore, the maximum net profit of the coal-fired power plant under the optimal dispatching system of the virtual power plant is sought. The specific steps of the ACCQPSO intelligent optimization algorithm are as follows:

[0074] S1: Establish the objective function;

[0075] In order to reflect the economic efficiency of VPP, the optimization goal is to maximize the net profit of VPP in cooperation with coal-fired power plants. The decision variables include revenue and cost. The revenue is the revenue from the internal power sales of the virtual power plant and the revenue from the sale of grid-connected electricity. The cost is the operating cost of the virtual power plant and coal-fired power plant and the part of the reward and punishment measures. The objective function is established as follows:

[0076]

[0077] Where: The revenue from electricity sales within the VPP; F is the income from VPP grid-connected electricity sales; sc ,pun is the reward and punishment for breach of contract; F vpp The operating cost of the VPP;

[0078] 1) The profit from selling electricity within the virtual power plant is:

[0079]

[0080] Where: is the internal electricity price of the virtual power plant at time t; The load required inside the virtual power plant; the amount of electricity that responds at any moment;

[0081] 2) The profit of the virtual power plant selling electricity to the power market is:

[0082]

[0083] Where: is the market electricity price at time t; is the actual power output of the market at time t;

[0084] 3) The rewards and penalties for breach of contract of virtual power plant are:

[0085]

[0086] Where: F sc,pun is the penalty fee for default electricity; λ is the penalty price factor for default; is the market electricity price at time t; They are the market contract power and actual power output at time t respectively;

[0087] 4) VPP operating costs mainly include coal-fired power plant fuel and unit operating costs, new energy power generation costs, coal-fired power plant start-up and shutdown costs, and maintenance costs, namely:

[0088]

[0089] Where: F dg F is the fuel unit operating cost; M For coal power plant operating costs; They are the response power and non-response power at time t respectively; They are the reward electricity price for responding load and the penalty electricity price for not responding load at time t;

[0090] Energy consumption cost of fuel unit:

[0091]

[0092] Where: P dg is the output of the fuel unit; a, b, c are the operating cost coefficients of the fuel unit;

[0093] The operating cost function of a coal-fired power plant is:

[0094]

[0095] Where: To provide power for coal-fired power plants; c is the penalty cost per unit of flue gas emission; e is the amount of flue gas generated per unit output of the coal-fired power plant;

[0096] S2: Optimize the dispatch of coal-fired power plants and virtual power plants using the adaptive crossover operator chaotic quantum particle swarm optimization algorithm (ACCQPSO). The power generation cost of virtual power plants and coal-fired power plants, the cost of new energy power generation, the start-up and shutdown cost, the operation cost, the maintenance cost, and the basic parameters of the algorithm are set for the partial benefits of coal-fired power plants and the power market. The parameters include the population size and the maximum number of iterations. The population is randomly initialized, the fitness of the population is calculated, the maximum net benefit value of the optimized dispatching system is obtained, and it is judged whether the iteration termination conditions are met. If so, the current cost is output as the maximum net benefit value. Otherwise, the initialized population is divided into several ordinary sub-populations, and the optimization is performed according to the individual behaviors in the initialized population.

[0097] S2.1, Logistics chaos map initialization population;

[0098] The various costs in the system, including the power generation costs of the VPP system and coal-fired power plants, the power generation costs of new energy, the fuel unit costs, the operating costs, the maintenance costs and the power market revenues, are represented by individual particles. The training set is input into the model for training, and the average absolute error obtained is used as the fitness function. At the same time, hybrid variables are added to the initial population.

[0099] By mapping Logistics into a chaotic sequence, the ergodic nature of chaotic motion is used to search for the optimal solution of the variables to be optimized, thereby improving the effectiveness and convergence speed of the algorithm. The Logistics mapping is as follows:

[0100] S n+1 =aS n (1-S n )

[0101] Where: S n is the initial sequence, S n ∈[0,1], a is the Logistics parameter representing the chaos initiator, a∈[0,4];

[0102] S2.2. Evaluate the fitness based on the initialized population: calculate the fitness value, the individual's historical optimal position, the group's optimal position, and the center of gravity of all particles; the population dynamically moves in the determined search space to find the position corresponding to the optimal fitness value as the best solution; in each iteration, the particle will track two extreme values, one is the optimal solution Pbest found by the particle itself so far (that is, the best net operating benefit of the VPP combined with the coal-fired power plant searched so far), and the other is the optimal solution Gbest found by the entire population so far (that is, the best net operating benefit of the VPP combined with the coal-fired power plant under the global search); its state is represented by the wave function ψ(x, t), and its square value represents the probability of x appearing at the position, and is represented by the attractor p k =(p1,p2,p3,...,p k ) is used as the center to constrain the quantum particles in the PSO algorithm, so that the net benefit can converge to its local P without explosion; the attractor p k for:

[0103] p k =ψ·P best_k +(1+ψ)G best

[0104] Where: P best_k The optimal net profit of VPP in the current iteration in conjunction with coal-fired power plants; G best The current global VPP cooperates with the coal power plant to achieve the best net profit; k is an attractor, used to update the kth net profit position;

[0105] S2.3. Improve the algorithm, optimize the search step size, and update the position solution; obtain the probability density function and distribution function by solving the Schrödinger equation, and use the Monte Carlo method to obtain the position of the kth net benefit. The position update is:

[0106]

[0107] Where: x k is the position of the kth net benefit, that is, the net benefit of the kth VPP in cooperation with the coal-fired power plant operation is updated; γ is an innovation parameter, which can control the convergence speed of the algorithm; M best The current average optimal solution; ψ and u are random numbers between (0,1) that follow a uniform distribution, with a probability of + or - of ±0.5 respectively;

[0108] The value of γ depends on the research scenario. It can be a fixed value or dynamically changed in a certain way. The selection method of the present invention is determined according to the decision-making of the virtual power plant to purchase and sell electricity. The formula is as follows:

[0109]

[0110] Where: T max , K r are the maximum and current iteration times respectively; α is the power purchase decision coefficient, β is the power sales decision coefficient, and R is the step size optimization factor;

[0111] S2.4, genetically encode the samples, introduce an adaptive crossover operator to increase the diversity of the population, improve the algorithm's ability to jump out of the local optimum, introduce the vertical crossover operator into the quantum particle swarm algorithm, and the vertical crossover is the arithmetic crossover between two different dimensions of a particle in a certain population; solve the problem of different value ranges of elements in different dimensions through normalization processing, and at the same time, only one offspring particle is generated for each operation, and only one dimension is updated;

[0112] Assuming that the d1th and d2th dimensions of the revenue update position X(j) participate in the vertical crossover, the trivial solution MS is generated according to the following formula vc (j,d1)

[0113] MS vc (j,d1)=r×X(j,d1)+(1-r)×X(j,d2)

[0114] Where: j∈N(1,M), d1,d2∈N(1,D), r∈[0,1], MS w (j, d1) is the d1-dimensional offspring generated by the vertical crossover of the d1-dimensional and d2-dimensional individual X(j); and the adaptive crossover probability improvement formula is crossovered to improve the algorithm's optimization ability and generate a new population; the adaptive crossover probability improvement formula is as follows:

[0115]

[0116] Where: p b1 and p b2 is a constant, P best M is the optimal net profit of VPP in the current iteration in conjunction with coal power plant operation. best The current average optimal solution, l' represents the fitness value corresponding to the better offspring particle after crossover operation in two different dimensions of a particle;

[0117] S2.4. Update the VPP to cooperate with the net profit of coal-fired power plant operation, introduce the spiral search strategy, improve the global search performance, calculate the fitness value of the new population, and update the fitness value; determine whether the crossover requirements are met. If they are met, perform vertical crossover, otherwise, go to the termination condition; the improved formula is as follows:

[0118]

[0119] X k+1 Provide net benefits for the operation of newer VPPs in conjunction with coal-fired power plants;

[0120] S2.5, check the termination condition; if the number of iterations is reached or the termination condition is met, terminate the iteration; otherwise, go to step S2.2;

[0121] S2.6. Obtain the net profit corresponding to the optimal fitness value, that is, the optimal net profit value of the coal-fired power plant under the virtual power plant optimization dispatching system.

[0122] In the present invention, the virtual power plant adopts advanced information network technology to integrate distributed resources such as renewable energy, energy storage system, flexible load into a whole, and realizes their coordinated scheduling to reduce the impact on the power grid when connected to the power grid, promote the complementarity of multiple energy sources, and meet the needs of the rapid development of the power market to increase energy utilization and reduce user power consumption; the coal-fired power plant is optimized and scheduled through the virtual power plant system, optimizes power generation resources to balance the instability of renewable energy, and combines energy storage equipment to store excess electricity to schedule the power generation of the coal-fired power plant, releases energy during peak power demand, and balances the supply and demand of the power grid, improves its flexibility and market competitiveness, and reduces the phenomenon of wind and solar power abandonment; manages the demand side according to demand response, and further optimizes the operation strategy of the coal-fired power plant; uses the virtual power plant to interact with the power market, including power trading, ancillary service market, etc., to maximize the economic benefits of the coal-fired power plant.

[0123] The present invention realizes more efficient, environmentally friendly and cost-effective electricity production and supply through the integration of technological innovation; increases investment in technological research and development, optimizes operation management, participates in market-oriented reforms, promotes the integrated development of coal-fired power and renewable energy, etc., to achieve sustainable development and promote the healthy and orderly development of the coal-fired power industry.

[0124] Example

[0125] The present invention is based on the current implementation plan for the construction of a virtual power plant in a city's energy construction in China's energy storage industry (2023-2025): building a virtual power plant platform for overall scheduling, aggregating two resource support systems of power resources and load resources, and building public buildings, charging (replacement) facilities, communication base stations, public lighting, energy storage power stations and other N industry fields and regional-level sub-virtual power plants; to promote the peak, peak-shaving and emergency standby capabilities of the power system, and improve the real-time balance and security level of the power system; including overall planning and reasonable layout, market-led policy guidance, and demonstration-first step-by-step implementation; 2023: build the main framework of the virtual power plant platform, and the overall adjustable capacity will reach more than 200,000 kilowatts; 2024: explore the operation mode of virtual power plants, initially establish operation specifications, and adjust The capacity will be increased to more than 800,000 kilowatts; by 2025: a market-oriented operation model will be formed to achieve "full connection" to energy resources, with an adjustable capacity of more than 1.3 million kilowatts, accounting for 5% of the maximum power load of the Chengdu power grid; its key tasks include the establishment of a virtual power plant management center; the construction of a virtual power plant platform, including a municipal platform and vertical scenarios, and regional sub-virtual power plant platforms; exploring virtual power plant operation mechanisms, including flexible load response, demand-side market-oriented response, etc.; building a virtual power plant standard and specification system; accelerating the construction of new energy storage facilities, focusing on the power supply needs of key places and key users, and focusing on power supply reliability issues such as tight supply and demand in local areas during the peak summer (winter), heavy overload of main transformers, and low voltage, giving priority to promoting the implementation of new energy storage pilot projects in key areas with obvious shortcomings.

[0126] The implementation plan for the construction of virtual power plants reflects the emphasis on energy transformation and the construction of new power systems. Through technological innovation and policy guidance, it promotes the optimization of energy structure and the green development of power systems. It actively promotes the construction of virtual power plants to build a new power system that is cleaner, lower-carbon, safer, more efficient, economical and intelligent. The construction of virtual power plants is not only of great significance to the local energy transformation and power system upgrade, but also has high research significance and reference value.

Claims

1. A coal power plant optimization dispatching system in cooperation with a virtual power plant, characterized in that: The system is composed of a virtual power plant system that coordinates and optimizes the resource scheduling of coal power plants, including a virtual power plant system and a coal power plant; The virtual power plant system includes distributed energy, energy storage system, controllable flexible load, virtual power plant management platform, power demand side management and power market.

2. According to claim 1, a coal power plant optimization dispatching system in cooperation with a virtual power plant is characterized in that: The optimization dispatching system aggregates coal-fired power plants and distributed energy resources to form a unified dispatching and management platform; then integrates the energy storage system to balance the power generation output of the coal-fired power plant, stores excess electricity when power demand is low, and releases energy during peak hours; then, based on the controllable flexible load information provided by the virtual power plant and the grid load balanced by demand-side management, the coal-fired power plant optimizes its own power generation plan and fuel usage, reducing the number of starts and stops.

3. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 1, characterized in that: The virtual power plant management platform includes an energy management system, prediction technology and optimization and control technology.

4. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 3 is characterized in that: The virtual power plant management platform combines intelligent terminals to aggregate data, demand response side and power market transaction side. First, the energy management system is responsible for data collection, monitoring and issuing control instructions through the Internet of Things, cloud computing and big data analysis technology to ensure real-time transmission of information and real-time monitoring of the operating status of coal-fired power plants; secondly, by collecting data, load forecasting and energy price forecasting, the power generation plan and operation strategy of the coal-fired power plant are optimized; finally, the power generation of the coal-fired power plant and other energy sources are intelligently dispatched according to the demand of the power grid and the real-time status of each energy source.

5. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 1, characterized in that: The power demand side management center is a demand response and intelligent dispatching center, which interacts with the power market and controllable loads to implement demand side management; Flexible load management can be achieved by using incentives or price signals to encourage users to reduce electricity consumption when electricity prices are high or increase electricity consumption when electricity prices are low.

6. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 1, characterized in that: The coal-fired power plant can reduce power generation when electricity demand peaks and increase power generation when demand is low.

7. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 1, characterized in that: The system realizes the best optimization dispatch of coal power plant power source through interactive integration of virtual power plant system and coal power plant to form a dispatch system; deeply interactively cooperates with coal power plant from the perspective of energy structure, integrated energy storage, response information and electricity market to optimize the operation strategy of coal power plant; and uses the chaotic quantum particle swarm optimization algorithm of adaptive crossover operator to seek the maximum net profit.

8. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 7, characterized in that: The specific operation steps of the chaotic quantum particle swarm optimization algorithm are as follows: (1) Establishing the objective function: To reflect the economic efficiency of VPP, the optimization goal is to maximize the net profit of VPP in cooperation with coal-fired power plants. The decision variables include revenue and cost. The revenue refers to the revenue from electricity sales within the virtual power plant and the revenue from the sale of grid-connected electricity. The cost refers to the operating cost of the virtual power plant and coal-fired power plant as well as the reward and punishment measures. The objective function is established as follows: Where: The revenue from electricity sales within the VPP; F is the income from VPP grid-connected electricity sales; sc,pun Rewards and penalties for breach of contract; vpp The operating cost of the VPP; (2) The coal-fired power plants and virtual power plants are optimally dispatched using the chaotic quantum particle swarm optimization algorithm with an adaptive crossover operator. The power generation costs of virtual power plants and coal-fired power plants, the power generation costs of new energy sources, the start-up and shutdown costs, the operating costs, the maintenance costs, and the basic parameters of the algorithm are set for the partial revenue of coal-fired power plants and the electricity market. The parameters include the population size and the maximum number of iterations. The population is randomly initialized, the fitness of the population is calculated, the maximum net profit value of the optimized scheduling system is obtained, and it is determined whether the iteration termination conditions are met. If so, the current cost is output as the maximum net profit value. Otherwise, the initialized population is divided into several ordinary sub-populations, and optimization is performed based on the individual behaviors in the initialized population.

9. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 8, characterized in that: The specific process of step (1) is as follows: (1.1) The profit from selling electricity within the virtual power plant is: Where: is the internal electricity price of the virtual power plant at time t; The load required within the virtual power plant; the amount of electricity that responds at any given moment; (1.2) The profit of a virtual power plant selling electricity to the power market is: Where: is the market electricity price at time t; is the actual power output of the market at time t; (1.3) The rewards and penalties for breach of the virtual power plant contract are: Where: F sc,pun is the penalty fee for default electricity; λ is the penalty price factor for default; is the market electricity price at time t; They are the market contract power and actual power output at time t respectively; (1.4) VPP operating costs mainly include coal-fired power plant fuel and unit operating costs, renewable energy power generation costs, coal-fired power plant start-up and shutdown costs, and maintenance costs, namely: Where: F dg F is the fuel unit operating cost; M For coal power plant operating costs; They are the response power and non-response power at time t respectively; They are the reward electricity price for responding load and the penalty electricity price for not responding load at time t; Energy consumption cost of fuel unit: Where: P dg is the output of the fuel unit; a, b, c are the operating cost coefficients of the fuel unit; The operating cost function of a coal-fired power plant is: Where: To provide power for coal-fired power plants; c is the penalty cost per unit of flue gas emission; e is the amount of flue gas generated per unit output of the coal-fired power plant.

10. The coal power plant optimization dispatching system in cooperation with a virtual power plant according to claim 8, characterized in that: The specific process of step (2) is as follows: (2.1) Initialize the population using the Logistics chaos map; represent the various costs in the system, including the VPP system and coal-fired power plant power generation costs, renewable energy power generation costs, fuel unit costs, operating costs, maintenance costs, and power market revenues, with individual particles. Input the training set into the model for training, and use the average absolute error obtained as the fitness function. At the same time, add hybrid variables to the initial population; use the Logistics map to transform it into a chaotic sequence, and use the ergodicity of chaotic motion to search for the optimal solution of the variables to be optimized, so as to improve the effectiveness and convergence speed of the algorithm. The Logistics map is as follows: S n+1 =aS n (1-S n ) Where: S n is the initial sequence, S n ∈[0,1], a is the Logistics parameter representing the chaos initiator, a∈[0,4]; (2.2) Evaluate the fitness based on the initialized population; calculate the fitness value, the individual's historical optimal position, the group's optimal position, and the center of gravity of all particles; the population dynamically moves in the determined search space to find the position corresponding to the optimal fitness value as the best solution; In each iteration, the particle will track two extreme values, one is the optimal solution Pbest found by the particle itself so far, and the other is the optimal solution Gbest found by the entire population so far; Its state is represented by the wave function ψ(x, t), whose square value represents the probability of x appearing at position t, and is represented by the attractor p k =(p1,p2,p3,...,p k ) is used as the center to constrain the quantum particles in the PSO algorithm, so that the net benefit can converge to its local P without explosion; the attractor p k for: p k =ψ·P best_k +(1+ψ)G best Where: P best_k The optimal net profit of VPP in the current iteration in conjunction with coal-fired power plants; G best The current global VPP cooperates with the coal power plant to achieve the best net profit; k is an attractor, used to update the kth net profit position; (2.3) Improve the algorithm and optimize the search step size; update the position solution, obtain the probability density function and distribution function by solving the Schrödinger equation, and use the Monte Carlo method to obtain the position of the kth net profit. The position update is: Where: x k is the position of the kth net profit, that is, the net profit of the kth VPP in cooperation with the coal-fired power plant operation is updated; γ is the innovation parameter, which can control the convergence speed of the algorithm; M best The current average optimal solution; ψ and u are random numbers between (0,1) that follow a uniform distribution, with a probability of + or - of ±0.5 respectively; The value of γ depends on the research scenario. It can be a fixed value or change dynamically in a certain way. The formula is as follows: Where: T max , K r are the maximum and current iteration times respectively; α is the power purchase decision coefficient, β is the power sales decision coefficient, and R is the step size optimization factor; (2.4) Genetically encode the samples, introduce an adaptive crossover operator to increase the diversity of the population, improve the algorithm's ability to jump out of the local optimum, and introduce a vertical crossover operator into the quantum particle swarm algorithm. Vertical crossover is an arithmetic crossover between two different dimensions of a particle in a certain population. Normalization is used to solve the problem of different value ranges of elements in different dimensions. At the same time, each operation only generates one offspring particle and only updates one dimension. Assuming that the d1th and d2th dimensions of the revenue update position X(j) participate in the vertical crossover, the trivial solution MS is generated according to the following formula vc (j,d1) MS vc (j,d1)=r×X(j,d1)+(1-r)×X(j,d2) Where: j∈N(1,M), d1,d2∈N(1,D), r∈[0,1], MS w (j, d1) is the d1-th dimension offspring generated by the vertical crossover of the d1-th dimension and the d2-th dimension of individual X(j); and the adaptive crossover probability improvement formula is crossovered to improve the algorithm's optimization ability and generate a new population; The adaptive crossover probability improvement formula is as follows: Where: p b1 and p b2 is a constant, P best M is the optimal net profit of VPP in the current iteration in conjunction with coal power plant operation. best The current average optimal solution, l' represents the fitness value corresponding to the better offspring particle after crossover operation in two different dimensions of a particle; (2.5) Update the VPP to cooperate with the net profit of coal-fired power plant operation, introduce the spiral search strategy, improve the global search performance, calculate the fitness value of the new population, and update the fitness value; determine whether the crossover requirements are met. If they are met, perform vertical crossover, otherwise, go to the termination condition; the improved formula is as follows: Where, X k+1 Provide net benefits for the operation of newer VPPs in conjunction with coal-fired power plants; (2.6) Check the termination conditions; If the number of iterations is reached or the termination condition is met, the iteration is terminated; otherwise, go to step 2); (2.7) and obtain the net profit corresponding to the optimal fitness value, that is, the optimal net profit value of the coal-fired power plant under the virtual power plant optimization dispatching system.

Images