Dynamic economic dispatching method and system for particle swarm optimization micro-grid

Abstract

The invention discloses a particle swarm optimization micro-grid dynamic economic dispatching method and system. The method comprises the following steps: acquiring a first parameter set of a target micro-grid, and establishing a first dispatching model based on the first parameter set; the first scheduling model comprises a first objective function and a first constraint condition set; presetting a first algorithm, and solving the first scheduling model according to the first algorithm; and performing dynamic economic dispatching of the micro-grid according to a solution result of the first dispatching model. The method and the system have higher efficiency, lower operation cost and stronger adaptability in practical application, and are particularly suitable for intelligent scheduling and optimization of modern micro-grids and isolated micro-grids.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid dynamic economic dispatch, and in particular, to a particle swarm optimization-based microgrid dynamic economic dispatch method and system. Background Art

[0002] The development of distributed energy is rapid. As an effective way to solve the power supply problem in remote areas, the operation and management of isolated microgrids have gradually received attention. Isolated microgrids integrate technologies such as distributed generation, energy storage systems, and demand response to achieve optimal dispatch of local energy resources and reduce dependence on traditional fossil fuels.

[0003] However, due to the high uncertainty and intermittency of renewable energy sources such as wind energy and solar energy, microgrids face many challenges in achieving supply-demand balance. Although traditional dispatch methods can partially consider these uncertainties, due to the lack of accurate dispatch of dynamic economic loads, the operation cost of microgrids is relatively high and the reliability is relatively low.

[0004] Most of the existing dispatch methods adopt static or single-time scale optimization and cannot flexibly cope with the output fluctuations of renewable energy and load changes. Especially during peak load periods, load shedding or insufficient power generation is likely to occur, thus affecting the stability and power supply reliability of the system.

[0005] The current dispatch scheme of microgrids makes less use of demand response and fails to fully exploit the adjustable potential of user-side loads, resulting in insufficient flexibility and economy in system dispatch and making it difficult to effectively smooth the fluctuations of renewable energy output. Summary of the Invention

[0006] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the specification of this application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the present invention provides a particle swarm optimization-based microgrid dynamic economic dispatch method and system, which can solve the problems mentioned in the background art.

[0009] To solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, the present invention provides a particle swarm optimization-based microgrid dynamic economic dispatch method, including:

[0011] Obtain the first parameter set of the target microgrid and establish a first scheduling model based on the first parameter set;

[0012] The first scheduling model includes a first objective function and a first set of constraint conditions;

[0013] Preset a first algorithm and solve the first scheduling model according to the first algorithm;

[0014] Perform dynamic economic dispatch of the microgrid according to the solution result of the first scheduling model.

[0015] As a preferred solution of the particle swarm optimization microgrid dynamic economic dispatch method of the present invention, wherein: the first scheduling model includes:

[0016] The first scheduling model is any model including a first objective function and a first set of constraint conditions;

[0017] The first objective function is any function of the total cost of the target microgrid;

[0018] The first set of constraint conditions at least includes power demand balance constraints, power constraints, and output constraints.

[0019] As a preferred solution of the particle swarm optimization microgrid dynamic economic dispatch method of the present invention, wherein: the obtaining of the first parameter set of the target microgrid at least includes:

[0020] The power generation costs of several dispatchable distributed generation units at different time periods;

[0021] The load reduction amount of the demand response plan within the different time periods;

[0022] The load reduction amount within the different time periods.

[0023] As a preferred solution of the particle swarm optimization microgrid dynamic economic dispatch method of the present invention, wherein: the output constraint includes setting the minimum output of several generators, the maximum output of several generators, the ramp-up rate limit of several generators, and the ramp-down rate limit of several generators.

[0024] As a preferred solution of the particle swarm optimization microgrid dynamic economic dispatch method of the present invention, wherein: the power demand balance constraint includes:

[0025] In each time period, the output of renewable energy generation and dispatchable generating units, plus the load reduced through demand response, must be equal to the total demand of the system and the load reduction when power supply is insufficient.

[0026] As a preferred embodiment of the particle swarm optimization-based dynamic economic dispatch method for a microgrid according to the present invention, wherein: the total cost of the target microgrid includes at least the output cost of dispatchable distributed generators, the incentive cost of the demand response plan, and the load shedding loss cost.

[0027] As a preferred embodiment of the particle swarm optimization-based dynamic economic dispatch method for a microgrid according to the present invention, wherein: the first algorithm is an improved particle swarm optimization algorithm.

[0028] In a second aspect, the present invention provides a particle swarm optimization-based dynamic economic dispatch system for a microgrid, comprising:

[0029] A model establishment module, configured to obtain a first parameter set of a target microgrid and establish a first dispatch model based on the first parameter set;

[0030] The first dispatch model includes a first objective function and a first constraint condition set;

[0031] A solution module, configured to preset a first algorithm and solve the first dispatch model according to the first algorithm;

[0032] A dispatch module, configured to perform dynamic economic dispatch of the microgrid according to the solution result of the first dispatch model.

[0033] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the steps of the method described above.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method described above.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a particle swarm optimization-based microgrid dynamic economic dispatch method and system, which obtains a first parameter set of a target microgrid and establishes a first dispatch model based on the first parameter set; the first dispatch model includes a first objective function and a first constraint condition set; a first algorithm is preset, and the first dispatch model is solved according to the first algorithm; the microgrid dynamic economic dispatch is carried out according to the solution result of the first dispatch model. By introducing an improved particle swarm optimization algorithm (PSO), the problem that the traditional dispatch algorithm is prone to fall into a local optimal solution when dealing with a complex energy system is solved. By restricting the particle velocity and combining the death penalty method and the repair method, the constraint conditions involved in the dispatch process are effectively handled, ensuring that the optimization process can not only meet the operation requirements of the system, but also improve the solution efficiency and accuracy. By integrating demand response and renewable energy power management, the economy and flexibility of the microgrid system are significantly improved. The prior art usually only focuses on the optimization of the power generation side, while the present invention further utilizes the load adjustment on the user side (demand response) and controls problems such as wind curtailment and light curtailment to achieve global load balance and optimization of power generation efficiency, reduce the system operation cost, and at the same time improve the ability to cope with the uncertainty of renewable energy output. These improvements enable the present application to have higher efficiency, lower operation cost and stronger adaptability in practical applications, and are particularly suitable for the intelligent dispatch and optimization of modern microgrids and isolated microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0037] Figure 1 It is a method flowchart of a particle swarm optimization-based microgrid dynamic economic dispatch method and system provided by an embodiment of the present invention;

[0038] Figure 2 It is an internal structure diagram of a computer device of a particle swarm optimization-based microgrid dynamic economic dispatch method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] Referring to Figure 1 - Figure 2 , which is the first embodiment of the present invention. This embodiment provides a particle swarm optimization-based microgrid dynamic economic dispatch method and system, including:

[0042] In the existing related technologies, there are some problems. For example, traditional microgrid dispatch methods often rely on fixed load forecasting and power generation plans, which may lead to low dispatch efficiency in actual operation due to inaccurate forecasting or unexpected events. In addition, when dealing with multi-objective optimization problems, these methods often have difficulty in simultaneously meeting the requirements of economy, reliability, and environmental impact.

[0043] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail on how to implement the particle swarm optimization-based microgrid dynamic economic dispatch method;

[0044] Figure 1 FIG. shows a flowchart of a method for a particle swarm optimization-based microgrid dynamic economic dispatch method and system, including:

[0045] S101, obtaining a first parameter set of the target microgrid and establishing a first dispatch model based on the first parameter set;

[0046] In an alternative embodiment, the target microgrid may include multiple distributed power sources, such as solar photovoltaic panels, wind turbines, micro gas turbines, etc., as well as energy storage devices such as battery energy storage systems. These distributed power sources and energy storage devices are interconnected through an intelligent control system to form a small power generation network.

[0047] In an alternative embodiment, the first parameter set of the target microgrid may include the real-time load data of the microgrid, the power generation capacity of the distributed power sources, the current state of the energy storage devices, and the real-time electricity price information of the power grid. Based on these parameters, a dynamic dispatch model can be constructed, which can adjust the dispatch strategy according to real-time data to achieve multi-objective optimization of economy, reliability, and environmental impact.

[0048] In an alternative embodiment, the manner of obtaining the first parameter set of the target microgrid can be achieved through sensors and data acquisition devices installed at key nodes of the microgrid.

[0049] In an optional embodiment, these devices can monitor and collect the operation data of the microgrid in real time, including but not limited to electrical parameters such as voltage, current, power, frequency, and environmental parameters such as ambient temperature and humidity. The collected data is transmitted to the central control system via wireless or wired means, and the central control system then integrates this data to form the real-time load data of the microgrid.

[0050] In an optional embodiment, the power generation capacity data of distributed power sources and the current state data of energy storage devices can also be obtained from their respective management systems through corresponding interfaces and protocols. The real-time electricity price information of the power grid can be updated in real time through the interface with the power grid operator. All these data together constitute the first parameter set of the target microgrid, providing a basis for constructing a dynamic scheduling model.

[0051] In the embodiment of the present application, the obtaining of the first parameter set of the target microgrid at least includes:

[0052] The power generation costs of several schedulable distributed generating units in different time periods;

[0053] The load reduction amount of the demand response plan in different time periods;

[0054] The load reduction amount in different time periods.

[0055] In an optional embodiment, the first parameter set may further include the number of schedulable distributed generating units, the number of time interval covered by the scheduling scheme, the power generation cost of the schedulable distributed generating unit in the t-th time period, the incentive amount provided by the demand response plan to participants, the load reduction amount of the demand response plan, the unit value of the economic loss caused by load reduction, and the load reduction amount.

[0056] It should be noted that obtaining the first parameter set of the target microgrid can provide accurate decision-making support for the economic dispatch of the microgrid. Through the real-time updated power generation costs, demand response plans, and load reduction information, the scheduling model can more accurately predict and respond to the operating state of the power grid, thereby optimizing the operating plans of generating units and the charge and discharge strategies of energy storage devices. This not only helps to reduce operating costs, but also improves the energy utilization efficiency and reliability of the microgrid. In addition, the obtaining of the first parameter set enables the microgrid to better adapt to electricity price fluctuations and maximize cost-effectiveness.

[0057] In the embodiment of the present application, the first scheduling model includes a first objective function and a first set of constraint conditions;

[0058] In the embodiment of the present application, the first scheduling model includes:

[0059] The first scheduling model is any model including a first objective function and a first set of constraint conditions;

[0060] The first objective function is any function of the total cost of the target microgrid;

[0061] The first set of constraint conditions at least includes power demand balance constraints, power constraints, and output constraints.

[0062] In an alternative embodiment, the first scheduling model can be designed in different ways to adapt to the specific requirements of different microgrids. For example, the first objective function can be a total cost minimization function, a total emissions minimization function, or a multi-objective optimization function that aims to consider both cost and environmental impact simultaneously. The first set of constraint conditions can then be adjusted according to the physical limitations and operating rules of the actual power grid to ensure that the output of the model meets both technical requirements and economic and environmental standards.

[0063] In an alternative embodiment, the first scheduling model can also integrate advanced prediction algorithms to improve the prediction accuracy of future power demand and renewable energy supply, thereby further enhancing the scheduling efficiency and reliability of the microgrid.

[0064] In an alternative embodiment, the first scheduling model can also introduce machine learning techniques to achieve deep learning and pattern recognition of the microgrid operation data. In this way, the scheduling model can self-learn and adapt to the volatility of the grid load and renewable energy supply, thereby optimizing the scheduling strategy. In addition, the model can also integrate a real-time monitoring system to adjust the operating state of the microgrid in real time to ensure optimal economic and environmental benefits in various situations.

[0065] In the embodiment of the present application, the output constraints include setting the minimum output of a number of generators, the maximum output of a number of generators, the ramp-up rate limit of a number of generators, and the ramp-down rate limit of a number of generators.

[0066] In the embodiment of the present application, the power demand balance constraint includes:

[0067] In each time period, the output of renewable energy generation and dispatchable generating units, plus the load reduced through demand response, must be equal to the total demand of the system and the load reduction when power supply is insufficient.

[0068] In the embodiment of the present application, the total cost of the target microgrid at least includes the output cost of dispatchable distributed generators, the incentive cost of the demand response plan, and the load reduction loss cost.

[0069] In an alternative embodiment, the first objective function can be achieved by minimizing the output cost of dispatchable distributed generators while considering the incentive cost of the demand response program and the load shedding loss cost. The optimization process of this objective function can adopt the particle swarm optimization algorithm, and search for the optimal solution through iteration to achieve the purpose of reducing the total operating cost of the microgrid. In the optimization process, each particle in the particle swarm algorithm represents a possible dispatch plan, and the position update of the particle depends on individual experience and social experience, that is, the particle's own historical optimal position and the group's historical optimal position. In this way, the particle swarm algorithm can effectively find the global optimal solution or approximate optimal solution in the search space, thus providing an effective solution for the economic dispatch of the microgrid.

[0070] In an alternative embodiment, the first objective function can also be achieved by minimizing the incentive cost of the demand response program while considering the output cost of dispatchable distributed generators and the load shedding loss cost. On this basis, the optimization process can also adopt the particle swarm optimization algorithm, and search for the optimal solution through iteration to further reduce the total operating cost of the microgrid. In this process, the combination of individual experience and social experience of the particle swarm algorithm enables the algorithm to effectively find the global optimal solution or approximate optimal solution in the search space, thus providing a more accurate solution for the economic dispatch of the microgrid.

[0071] In an alternative embodiment, the first objective function can also be achieved by minimizing the load shedding loss cost while considering the output cost of dispatchable distributed generators and the incentive cost of the demand response program. In this way, the particle swarm optimization algorithm continuously adjusts the position of the particle during the iteration process to find the optimal dispatch plan that can balance costs and losses. In the particle swarm algorithm, the flight speed and direction of each particle will be dynamically adjusted according to its individual experience and group experience, ensuring that the algorithm can cover a wide area of the search space and quickly converge to the optimal solution. Finally, this method can provide an efficient economic dispatch strategy for the microgrid that comprehensively considers costs, incentives, and losses to cope with various dynamic changes in the power grid operation.

[0072] In the embodiment of the present application, the first dispatch model is designed as follows:

[0073] The first dispatch model designs the output cost of dispatchable distributed generators, the incentive cost of the demand response program, and the load shedding loss cost:

[0074]

[0075] Among them, J is the total cost of the objective function; NG is the number of dispatchable distributed generator sets; T is the number of time intervals covered by the dispatch plan; Cost i (P i,t) is the power generation cost of the i-th dispatchable distributed generation unit in the t-th time period; inc t is the incentive amount provided to participants by the demand response plan within the t-th time period; P DR,t is the load reduction amount of the demand response plan within the t-th time period; VOLL t is the unit value of the economic loss caused by load reduction within the t-th time period; P shed,t is the load reduction amount within the t-th time period.

[0076] In an alternative embodiment, the calculation formula for the output cost of the dispatchable distributed generator is as follows:

[0077] Cost i (P i,t ) = a i (P i,t ) 2 +b i P i,t +c i

[0078] where P i,t is the power of the i-th DG at time t, and a i , b i , c i are the cost coefficients of the dispatchable distributed generator.

[0079] In the embodiment of the present application, for the power demand balance constraint, within each time period, the balance between the power supply and demand of the system: the output of renewable energy generation and dispatchable generating units, plus the load reduced through demand response, must be equal to the total demand of the system and the load reduction when the power supply is insufficient:

[0080]

[0081] where P p,t is the available power of the p-th new energy unit at time t; curtail p,t is the abandoned wind and light power of the p-th new energy unit at time t; N ren is the number of new energy units.

[0082] In the embodiment of the present application, a demand response power constraint is established, and the load reduction amount of the demand response plan needs to meet the maximum power constraint:

[0083] 0 ≤ P DR,t ≤ hD t

[0084] where h is the ratio of the reducible demand to the microgrid demand.

[0085] In the embodiment of the present application, the output constraint of the dispatchable distributed generator is as follows:

[0086] P i,min ≤P i,t ≤P i,max

[0087] P i,t -P i,t-1 ≤RU i

[0088] P i,t-1 -P i,t ≤RD i

[0089] Among them, P i,min is the minimum output of the i-th generator; P i,max is the maximum output of the i-th generator; RU i is the ramp rate limit of the i-th generator; RD i is the down ramp rate limit of the i-th generator.

[0090] It should be noted that obtaining the first parameter set of the target microgrid and establishing the first scheduling model based on the first parameter set can provide scientific decision-making support for the operation of the microgrid, optimize the allocation of power generation resources, and reduce the operating cost. In practical applications, this method and system help to improve the reliability and stability of the microgrid, and at the same time meet the dual goals of environmental protection and economic benefits.

[0091] S102, preset the first algorithm, and solve the first scheduling model according to the first algorithm;

[0092] In an alternative embodiment, the first algorithm can be the particle swarm optimization algorithm, and the first scheduling model can be solved by introducing the particle swarm optimization algorithm. This algorithm simulates the foraging behavior of bird flocks and uses the interaction and information sharing among individuals in the group to find the optimal solution. In the context of microgrid dynamic economic dispatch, each particle represents a possible dispatch plan, and the particle swarm algorithm iteratively updates the position and velocity of the particles in order to find the optimal dispatch plan that minimizes the value of the first objective function.

[0093] In another alternative embodiment, in each iteration, the position update of the particles takes into account the first set of constraint conditions to ensure that all dispatch plans meet the power demand balance, power and output constraints. In this way, the particle swarm optimization algorithm can effectively handle the complexity and multi-objective characteristics of the microgrid dynamic economic dispatch problem and provide an efficient and economic dispatch strategy for microgrid operators.

[0094] In an alternative embodiment, the first algorithm may be a genetic algorithm, and the first scheduling model may also be further optimized by introducing a genetic algorithm. A genetic algorithm is a search algorithm that simulates natural selection and genetic principles. It iteratively evolves candidate solutions through operations such as selection, crossover, and mutation in order to find the global optimal solution. In the dynamic economic dispatch of a microgrid, a genetic algorithm can be used to generate and evaluate multiple dispatch schemes. Through continuous iteration, schemes with high fitness are selected for crossover and mutation, thereby gradually approaching the optimal dispatch strategy. This method can handle the non-linear, multi-peak, and uncertain characteristics in the dispatch problem and provide support for the efficient operation of the microgrid.

[0095] In an alternative embodiment, the first algorithm may be a fuzzy logic control strategy algorithm, and the first scheduling model may also be enhanced in terms of dispatch flexibility and adaptability by introducing a fuzzy logic control strategy. Fuzzy logic control allows the system to make more reasonable decisions in the face of uncertainty and fuzzy information, which is particularly important when dealing with fluctuations in renewable energy output and load changes. Through fuzzyfication, uncertain input data can be converted into fuzzy sets, and reasoning can be performed through a fuzzy rule base. Finally, a fuzzy output is obtained, which is then defuzzified to convert into specific dispatch instructions. This method can effectively cope with various uncertain factors in the operation of the microgrid and improve the robustness of the dispatch strategy.

[0096] In the embodiment of the present application, the first algorithm is an improved particle swarm optimization algorithm. Specifically:

[0097] In PSO, a group of particles traverse the search space and attempt to find a globally near-optimal feasible solution for the optimization problem. The best position visited by a particle is called its personal best position, and the best position visited by the entire swarm is called the global best position. The particle position is represented as the following formula

[0098]

[0099] where X j is the j-th particle.

[0100] All particles are randomly initialized. During each iteration, the particle velocity and position are updated according to the current velocity, current position, individual best, and global best states. The update relationship is as follows:

[0101] V j (it + 1) = ωV j (it) + C1r1(best j -X j (it)) + C2r2(best g -X j (it))

[0102] X j (it + 1) = X j (it) + V j (it + 1)

[0103] where V j is the velocity vector of the j-th particle; C1 and C2 are acceleration coefficients; best j is the personal best state of the j-th particle; best g is the global best state; the symbol ω is the inertia weight; r1 and r2 are random numbers.

[0104] It should be noted that by updating the PSO particle state through this method and restricting the particle velocity within a predetermined interval, the PSO performance can be improved. In each iteration, once the position is updated, the particle is evaluated, and its personal best state and global best state are updated. Therefore, the global best state, as the solution to the optimization problem, will improve with iterations. The intelligent traversal of the particle within the search space will continue until the predefined stop criterion is met. In this application, the penalty method and the repair method are used to handle constraints, where infeasible particles are transformed into feasible particles. Each particle that violates the ramp-up / down rate limit will be repaired.

[0105] It should also be noted that by presetting the first algorithm and solving the first scheduling model according to the first algorithm, the calculation time can be effectively reduced and the solution efficiency can be improved. By optimizing the parameter settings of the algorithm, the economy and reliability of the microgrid operation can be further enhanced. When dealing with large-scale microgrid systems, the algorithm can maintain good convergence and stability. The reasonable scheduling of various energy sources in the microgrid is realized, ensuring the efficient utilization of energy and the minimization of costs. The applicability and superiority of this method in different scenarios are verified through simulation experiments, providing a new solution for the dynamic economic scheduling of the microgrid.

[0106] S103. Conduct dynamic economic scheduling of the microgrid according to the solution result of the first scheduling model.

[0107] In an optional embodiment, conducting dynamic economic scheduling of the microgrid according to the solution result of the first scheduling model can be achieved through a central control system. This system can monitor the operation status of the microgrid in real time and dynamically adjust the output power of the generator sets according to various information such as grid load, generation cost, and environmental factors.

[0108] In an optional embodiment, the optimization algorithm built into the system will continuously adjust the scheduling strategy according to real-time data to achieve the goal of economic operation.

[0109] In an alternative embodiment, the system may also have a prediction function, which can predict future energy demand and supply based on historical data, weather forecasts and other information, so as to make scheduling decisions in advance and further improve the operating efficiency and economic benefits of the microgrid.

[0110] In summary, the present invention proposes a particle swarm optimization microgrid dynamic economic dispatch method, which obtains a first parameter set of the target microgrid and establishes a first dispatch model based on the first parameter set; the first dispatch model includes a first objective function and a first constraint set; a first algorithm is preset, and the first dispatch model is solved according to the first algorithm; the microgrid dynamic economic dispatch is carried out according to the solution result of the first dispatch model. By introducing an improved particle swarm optimization algorithm (PSO), the problem that the traditional dispatch algorithm is prone to fall into a local optimal solution when dealing with complex energy systems is solved. By restricting the particle velocity and combining the death penalty method and the repair method, the constraint conditions involved in the dispatch process are effectively processed, ensuring that the optimization process can not only meet the operating requirements of the system, but also improve the solution efficiency and accuracy. By integrating demand response and renewable energy power management, the economy and flexibility of the microgrid system are significantly improved. The prior art usually only focuses on the optimization of the power generation side, while the present invention further utilizes the load adjustment on the user side (demand response) and controls problems such as wind curtailment and light curtailment to achieve global load balance and optimization of power generation efficiency, reduce the system operation cost, and at the same time improve the ability to cope with the uncertainty of renewable energy output. These improvements make the present application have higher efficiency, lower operation cost and stronger adaptability in practical applications, and are particularly suitable for the intelligent dispatch and optimization of modern microgrids and isolated microgrids.

[0111] Embodiment 2

[0112] In a preferred embodiment, a specific operation step is designed according to the particle swarm optimization microgrid dynamic economic dispatch method in Embodiment 1 to achieve the dynamic economic dispatch of the target microgrid, which is as follows:

[0113] First, construct a microgrid dynamic economic load dispatch model. The decision variables of this application include the power of schedulable distributed generators, the curtailment power of new energy generating units, the load shedding and the curtailment power of demand response plans.

[0114] Secondly, construct the total optimization cost including the output cost of schedulable distributed generators, the incentive cost of demand response plans and the load shedding loss cost:

[0115]

[0116] Where J is the total cost of the objective function; NG is the number of schedulable distributed generating units; T is the number of time intervals covered by the dispatch plan; Cost i(P i,t ) is the power generation cost of the $i$-th dispatchable distributed generation unit in the $t$-th time period; inc t is the incentive amount provided to participants by the demand response plan in the $t$-th time period; P DR,t is the load reduction amount of the demand response plan in the $t$-th time period; VOLL t is the unit value of the economic loss caused by load reduction in the $t$-th time period; P shed,t is the load reduction amount in the $t$-th time period.

[0117] Again, the calculation formula for the output cost of the dispatchable distributed generator is as follows:

[0118] Cost i (P i,t ) = a i (P i,t ) 2 + b i P i,t + c i

[0119] where P i,t is the power of the $i$-th DG at time $t$, a i , b i , c i are the cost coefficients of the dispatchable distributed generator.

[0120] Again, establish the power demand balance constraint. In each time period, the balance between the power supply and demand of the system: the power generation of renewable energy and the output of dispatchable generation units, plus the load reduced through demand response, must be equal to the total demand of the system and the load reduction when the power supply is insufficient:

[0121]

[0122] where P p,t is the available power of the $p$-th new energy unit at time $t$; curtail p,t is the wind and light abandonment power of the $p$-th new energy unit at time $t$; N ren is the number of new energy units.

[0123] Again, establish the demand response power constraint. The load reduction amount of the demand response plan needs to meet the maximum power constraint:

[0124] 0 ≤ P DR,t ≤ hD t

[0125] where $h$ is the ratio of the reducible demand to the microgrid demand.

[0126] The output constraint of the dispatchable distributed generator is as follows:

[0127] P i,min ≤P i,t ≤P i,max

[0128] P i,t -P i,t-1 ≤RU i

[0129] P i,t-1 -P i,t ≤RD i

[0130] Among them, P i,min is the minimum output of the i-th generator; P i,max is the maximum output of the i-th generator; RU i is the ramp rate limit of the i-th generator; RD i is the down ramp rate limit of the i-th generator.

[0131] Finally, the particle swarm optimization (PSO) is used to solve the problem. In PSO, a swarm of particles traverses the search space and tries to find a near-global feasible solution for the optimization problem. The best position visited by a particle is called its personal best position, and the best position visited by the entire swarm is called the global best position. The particle position is represented as the following formula

[0132]

[0133] where X j is the j-th particle.

[0134] All particles are randomly initialized. During each iteration, the particle velocity and position are updated according to the current velocity, current position, individual best, and global best states. The update relationships are as follows:

[0135] V j (it + 1) = ωV j (it) + C1r1(best j - X j (it)) + C2r2(best g - X j (it))

[0136] X j (it + 1) = X j (it) + V j (it + 1)

[0137] where, V j is the velocity vector of the j-th particle; C1, C2 are acceleration coefficients; best j is the individual best state of the j-th particle; best gis the global best state; the symbol ω is the inertia weight; r1 and r2 are random numbers.

[0138] By updating the PSO particle state with this method, restricting the particle velocity within a predetermined interval can improve the PSO performance. In each iteration, once the position is updated, the particle is evaluated, and its individual best state and global best state are updated. Therefore, the global best state, as the solution to the optimization problem, improves with iterations. The intelligent traversal of the particle within the search space will continue until a predefined stop criterion is met. In this application, the penalty method and the repair method are used to handle constraints, where infeasible particles are transformed into feasible particles. Each particle that violates the ramp-up / down rate limit is repaired.

[0139] Embodiment 3

[0140] This embodiment also provides a particle swarm optimization microgrid dynamic economic dispatch system, including:

[0141] A model establishment module, configured to obtain a first parameter set of a target microgrid and establish a first dispatch model based on the first parameter set;

[0142] The first dispatch model includes a first objective function and a first constraint condition set;

[0143] A solution module, configured to preset a first algorithm and solve the first dispatch model according to the first algorithm;

[0144] A dispatch module, configured to perform microgrid dynamic economic dispatch according to the solution result of the first dispatch model.

[0145] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned each module.

[0146] This embodiment also provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 2As shown in the figure. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a particle swarm optimization microgrid dynamic economic dispatch method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0147] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0148] Obtain a first parameter set of the target microgrid, and establish a first dispatch model based on the first parameter set;

[0149] The first dispatch model includes a first objective function and a first set of constraint conditions;

[0150] Preset a first algorithm, and solve the first dispatch model according to the first algorithm;

[0151] Perform microgrid dynamic economic dispatch according to the solution result of the first dispatch model.

[0152] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0153] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0155] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0157] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0158] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, then this application is also intended to include these modifications and variations.

Claims

1. A particle swarm optimization-based microgrid dynamic economic dispatch method, characterized in that, Including: Obtain a first parameter set of the target microgrid, and establish a first scheduling model based on the first parameter set; The first scheduling model includes a first objective function and a first constraint condition set; Preset a first algorithm, and solve the first scheduling model according to the first algorithm; Perform dynamic economic scheduling of the microgrid according to the solution result of the first scheduling model.

2. The particle swarm optimization microgrid dynamic economic dispatch method according to claim 1, characterized in that The first scheduling model includes: The first scheduling model is any model including a first objective function and a first constraint condition set; The first objective function is any function of the total cost of the target microgrid; The first constraint condition set at least includes power demand balance constraint, power constraint and output constraint.

3. The particle swarm optimization-based microgrid dynamic economic dispatch method according to claim 2, characterized in that The obtaining of the first parameter set of the target microgrid at least includes: The power generation costs of several dispatchable distributed generation units in different time periods; The load reduction amount of the demand response plan in different time periods; The load reduction amount in different time periods.

4. The particle swarm optimization-based microgrid dynamic economic dispatch method according to claim 3, characterized in that The output constraint includes setting the minimum output of several generators, the maximum output of several generators, the ramp-up rate limit of several generators and the ramp-down rate limit of several generators.

5. The particle swarm optimization-based microgrid dynamic economic dispatch method according to claim 4, characterized in that The power demand balance constraint includes: In each time period, the output of renewable energy generation and dispatchable generating units, plus the load reduced through demand response, must be equal to the total demand of the system and the load reduction when power supply is insufficient.

6. The particle swarm optimization-based microgrid dynamic economic dispatch method according to claim 5, characterized in that The total cost of the target microgrid at least includes the output cost of dispatchable distributed generators, the incentive cost of the demand response plan and the load reduction loss cost.

7. The particle swarm optimization-based microgrid dynamic economic dispatch method according to claim 6, wherein The first algorithm is an improved particle swarm optimization algorithm.

8. A particle swarm optimization-based microgrid dynamic economic dispatch system, characterized in that, Including: A model establishment module, configured to obtain a first parameter set of the target microgrid, and establish a first scheduling model based on the first parameter set; The first scheduling model includes a first objective function and a first constraint condition set; A solving module, configured to preset a first algorithm and solve the first scheduling model according to the first algorithm; A scheduling module, configured to perform dynamic economic scheduling of the microgrid according to the solution result of the first scheduling model.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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