CN-122001006-A - Multi-target scheduling method of photovoltaic energy storage hybrid equipment

CN122001006ACN 122001006 ACN122001006 ACN 122001006ACN-122001006-A

Abstract

The invention relates to the technical field of photovoltaic energy storage scheduling, in particular to a multi-target scheduling method of photovoltaic energy storage hybrid equipment, which aims to solve the problems of poor comprehensive performance, large scheme selection deviation, unstable equipment operation and the like of the traditional scheduling method; the method comprises the steps of collecting equipment operation and external environment parameters to construct a database, constructing a multi-objective scheduling model containing economic, environmental protection and operation stability targets, synchronously setting constraint conditions, adopting an improved weight self-adaptive genetic algorithm solving model to obtain a pareto optimal solution set, screening an optimal scheduling scheme with the highest comprehensive score through a analytic hierarchy process, regulating and controlling equipment operation in real time based on the scheme and feeding back effects to the database to form a closed loop, realizing multi-objective balance optimization, improving the rationality and the accuracy of the scheduling scheme, avoiding scheduling scheme selection deviation caused by unreasonable weight setting or single evaluation method, ensuring the accuracy and the applicability of the scheduling scheme, and providing scientific support for optimizing operation of the photovoltaic energy storage hybrid equipment.

Inventors

WU YAN
XU CHENHUI
CHEN LIANG

Assignees

上海电气(江苏)综合能源服务有限公司

Dates

Publication Date: 20260508
Application Date: 20251230

Claims (10)

1. The multi-target scheduling method of the photovoltaic energy storage hybrid equipment is characterized by comprising the following steps of: step S1, collecting operation parameters and external environment parameters of the photovoltaic energy storage hybrid equipment, and establishing a parameter database; s2, constructing a multi-target scheduling model based on the parameter database, wherein the multi-target scheduling model comprises an economical efficiency target, an environmental protection target and an operation stability target; Step S3, solving the multi-objective scheduling model by adopting an improved weight self-adaptive genetic algorithm to obtain a pareto optimal solution set; s4, screening from the pareto optimal solution set to obtain an optimal scheduling scheme; And S5, regulating and controlling the running state of the photovoltaic energy storage hybrid equipment in real time according to the optimal scheduling scheme, and feeding back a regulating and controlling effect to a parameter database in real time.
2. The multi-target scheduling method of the photovoltaic energy storage hybrid device according to claim 1, wherein the operation parameters in the step S1 include output power of a photovoltaic module, charge and discharge power of an energy storage unit, residual electric quantity of the energy storage unit, charge and discharge efficiency of the energy storage unit and device operation loss power, and the external environment parameters include illumination intensity, environment temperature, wind speed, power grid load requirement and power grid real-time electricity price.
3. The multi-objective scheduling method of a photovoltaic energy storage hybrid device according to claim 1, wherein the economical objective in step S2 is to minimize the comprehensive operation cost of the photovoltaic energy storage hybrid device, and an economical objective function is constructed as follows: ; wherein each parameter is defined as follows, Is the comprehensive operation cost; The electricity purchasing cost is realized; Maintenance costs for the equipment; the method is used for selling electricity and income for surfing the Internet; Is the cost of power loss, wherein, the cost of electricity purchase The product of the real-time electricity price and the electricity purchasing quantity of the power grid is calculated to obtain electricity selling benefits And calculating the product of the internet power price and the electricity sales quantity.
4. The multi-objective scheduling method of a photovoltaic energy storage hybrid device according to claim 1, wherein the environmental protection objective in step S2 is to minimize carbon emissions during operation of the photovoltaic energy storage hybrid device, and the environmental protection objective function is constructed as follows: ; wherein each parameter is defined as follows, Is carbon emission; Is a scheduling period; The unit carbon emission coefficient for supplying power to the power grid; Is that Purchase power at moment; A unit carbon emission coefficient for the auxiliary equipment; Is that The consumption power of the auxiliary equipment at the moment.
5. The multi-objective scheduling method of a photovoltaic energy storage hybrid device according to claim 1, wherein the operation stability objective in step S2 is to minimize the fluctuation range of the output power of the photovoltaic energy storage hybrid device, and the operation stability objective function is constructed as follows: ; wherein each parameter is defined as follows, Is the output power fluctuation coefficient; Is that The output power of the time-of-day device; Which is the average output power of the device during the scheduling period.
6. The multi-target scheduling method of the photovoltaic energy storage hybrid device according to claim 1, wherein constraint conditions are required to be set when the multi-target scheduling model is constructed in the step S2, the constraint conditions comprise energy storage unit constraint, power balance constraint and device operation safety constraint, and the energy storage unit constraint is as follows: ; wherein each parameter is defined as follows, Is that The state of charge of the energy storage unit at any time; The minimum allowable residual capacity of the energy storage unit; the maximum allowable residual capacity of the energy storage unit; the power balance constraint is as follows: ; wherein each parameter is defined as follows, Is that Output power of the photovoltaic module at any time; Is that Charging power of the time energy storage unit; Is that The discharge power of the time energy storage unit; Is that And the consumed power of the load carried by the photovoltaic energy storage hybrid device is at the moment.
7. The multi-objective scheduling method of a photovoltaic energy storage hybrid device according to claim 1, wherein the optimization process of the improved weight adaptive genetic algorithm in step S3 comprises: S31, initializing a population, taking a charge and discharge power sequence of an energy storage unit as a chromosome code, and setting initial values of population scale, maximum iteration times, crossover probability and variation probability, wherein the chromosome code rule is that the charge and discharge power of the energy storage unit at each moment in a dispatching cycle is taken as a code element, the code length is equal to the time segmentation number of the dispatching cycle, and the value range of each code bit corresponds to the allowable interval of the charge and discharge power of the energy storage unit; s32, calculating an adaptability value of each individual, wherein the adaptability value is obtained based on multi-target weighted summation, and the weight coefficient is dynamically adjusted through an adaptive strategy; s33, selecting by adopting a roulette selection method, and reserving individuals with higher fitness values; S34, performing cross operation by adopting a two-point cross method, wherein the cross probability is dynamically adjusted according to the individual fitness value, and the higher the fitness value is, the lower the cross probability is; s35, carrying out mutation operation by adopting a random mutation method, wherein the mutation probability is dynamically adjusted according to the individual fitness value, so that the algorithm is prevented from falling into local optimum; S36, judging whether the maximum iteration times are reached, if not, returning to the step S32, and if so, outputting the pareto optimal solution set.
8. The multi-objective scheduling method of a photovoltaic energy storage hybrid device according to claim 7, wherein the adaptive adjustment formula of the weight coefficient in step S32 is as follows: ; wherein each parameter is defined as follows, Is the first The objective function is at The weight coefficient of the generation; Is the first The objective function is at The weight coefficient of the generation; is an adaptive adjustment factor; the average fitness value of the current population; Is the first The objective function is at The adaptive adjustment formula is applicable to the condition that When (1) In the time-course of which the first and second contact surfaces, Therein, wherein 。
9. The multi-objective scheduling method for the photovoltaic energy storage hybrid device according to claim 1, wherein the specific process of screening the optimal scheduling scheme in the step S4 is that comprehensive evaluation is performed on each solution in the pareto optimal solution set by using a analytic hierarchy process, and each evaluation index score is obtained by normalizing an objective function, wherein the economic score and the economic score are obtained by adopting a method of performing analytic hierarchy process Negative correlation, environmental protection score and And (3) forming a negative correlation, wherein the stability score is inversely correlated with f 3 , the normalization range is [0,100], a solution with the highest comprehensive score is selected as an optimal scheduling scheme according to an evaluation result, and the evaluation indexes of the analytic hierarchy process comprise an economical efficiency score, an environmental protection score and a stability score, and the weights of the indexes are determined according to actual application scenes.
10. The multi-target scheduling method of the photovoltaic energy storage hybrid equipment is characterized in that the real-time regulation and control process in the step S5 comprises the steps of collecting equipment operation parameters in real time, comparing the equipment operation parameters with parameter thresholds in an optimal scheduling scheme, controlling a charge-discharge switch of an energy storage unit to realize parameter correction on a photovoltaic module with an angle regulation function by regulating a tracking angle of the photovoltaic module if deviation exists, and feeding back the regulated operation parameters to a parameter database to provide data support for next round of scheduling.

Description

Multi-target scheduling method of photovoltaic energy storage hybrid equipment Technical Field The invention relates to the technical field of photovoltaic energy storage scheduling, in particular to a multi-target scheduling method of photovoltaic energy storage hybrid equipment. Background With the aggravation of global energy crisis and the continuous rise of environmental protection consciousness, the development and utilization of renewable energy become a research hotspot in the energy field. The solar energy is used as a clean and renewable energy source, has the remarkable advantages of wide distribution, abundant reserves and the like, and has important significance for optimizing an energy structure, reducing carbon emission and realizing a double-carbon target in large-scale development and utilization. The photovoltaic energy storage hybrid device is used as a key carrier for solar energy utilization, and the photovoltaic assembly and the energy storage unit are organically combined, so that the problems of intermittence and fluctuation of photovoltaic output can be effectively solved, and the solar energy absorption efficiency is improved, and the photovoltaic energy storage hybrid device is widely applied to scenes such as a distributed energy system, a micro-grid and a large-scale photovoltaic power station. However, the operation scheduling photovoltaic output of the photovoltaic energy storage hybrid device is obviously influenced by natural environment factors such as illumination intensity, environmental temperature and the like, multiple targets such as economy, environmental protection and operation stability are required to be considered in the operation process of the device, in the prior art, a scheduling method aiming at the photovoltaic energy storage device is focused on single target optimization, for example, only the operation cost is minimized as a target, or only the output stability is focused, the coupling and balance relation among multiple targets cannot be fully considered, the comprehensive performance of a scheduling scheme is poor, the multiple targets are considered in some multi-target scheduling methods, but in the construction process of an objective function, the consideration of the operation parameters of the device and the external environment parameters is not comprehensive enough, the complexity of the actual operation condition cannot be accurately reflected, and the multi-target scheduling method of the photovoltaic energy storage hybrid device is provided based on the multiple targets so as to solve the problems. Disclosure of Invention Aiming at the technical problems in the prior art, the invention provides a multi-target scheduling method of photovoltaic energy storage hybrid equipment. The technical scheme for solving the technical problems is as follows, the multi-target scheduling method of the photovoltaic energy storage hybrid equipment comprises the following steps: step S1, collecting operation parameters and external environment parameters of the photovoltaic energy storage hybrid equipment, and establishing a parameter database; s2, constructing a multi-target scheduling model based on the parameter database, wherein the multi-target scheduling model comprises an economical efficiency target, an environmental protection target and an operation stability target; Step S3, solving the multi-objective scheduling model by adopting an improved weight self-adaptive genetic algorithm to obtain a pareto optimal solution set; s4, screening from the pareto optimal solution set to obtain an optimal scheduling scheme; And S5, regulating and controlling the running state of the photovoltaic energy storage hybrid equipment in real time according to the optimal scheduling scheme, and feeding back a regulating and controlling effect to a parameter database in real time. In a preferred embodiment, the operation parameters in step S1 include output power of the photovoltaic module, charge and discharge power of the energy storage unit, remaining power of the energy storage unit, charge and discharge efficiency of the energy storage unit, and running loss power of the device, and the external environment parameters include illumination intensity, environment temperature, wind speed, power grid load demand, and real-time power price of the power grid. In a preferred embodiment, the economic objective described in step S2 is aimed at minimizing the overall operating cost of the photovoltaic energy storage hybrid device, and the economic objective function is constructed as follows: ; wherein each parameter is defined as follows, Is the comprehensive operation cost; The electricity purchasing cost is realized; Maintenance costs for the equipment; the method is used for selling electricity and income for surfing the Internet; Is the cost of power loss, wherein, the cost of electricity purchase The product of the real-time electricity price and the electricity purc