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This article was submitted to Smart Grids, a section of the journal Frontiers in Energy Research

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The uncertainty of wind resources is one of the main reasons for wind abandonment. Considering the uncertainty of wind power prediction, a robust optimal dispatching model is proposed for the wind fire energy storage system with advanced adiabatic compressed air energy storage (AA-CAES) technology. Herein, the operation constraints of the power plant and constraints of the reserved capacity are defined according to the operation characteristics of AA-CAES. Based on the limited scenario method, a solution framework is proposed to achieve the optimal robustness and economical operation of the system, which provides a new way for the application of the intelligent algorithm in the robust optimal dispatching. Specifically, a novel equilibrium optimization algorithm is employed to solve the optimal dispatching problem, which has good global search performance. The proposed solution is validated through simulations based on the IEEE-39 node system. The simulation results verify the effectiveness of the proposed dispatching model and the intelligent solver.

Facing the challenge of global warming and energy crisis, wind power generation has been rapidly developed in recent years (

The application of energy storage system is one of the common methods to reduce the wind abandonment rate of wind farm. As a typical energy storage technology, conventional compressed air energy storage (C-CAES) has been widely used in integrated energy system. It was proved that the utilization of C-CAES can increase the flexibility of comprehensive energy system and improve economic benefits (

At present, AA-CAES has been applied into the integrated energy system dispatching. The energy system dispatching model based on AA-CAES was studied, and its value was analyzed in monopoly power market, energy market and reserve market (

Generally, the optimal dispatching model of the power system usually presents the characteristics of nonlinearity, high dimension, strong coupling and multi constraints. Most optimal dispatching problems are solved for the whole time series. When using intelligent algorithms, there are problems such as high dimension and difficulty in meeting constraints. Based on these problems, an optimization framework suitable for the utilization of intelligent algorithms is proposed, which has better solution results and shorter solution time. Specifically, to solve the formulated optimal dispatching problem, it is employed a novel equilibrium optimization (EO) algorithm (

Combining AA-CAES with renewable energy, a robust optimal scheduling model is established for the wind fire energy storage system, in which the limited scenario method is used to represent the uncertainty of wind power prediction. In order to solve the dispatching scheme corresponding to minimizing the comprehensive cost in the extreme scenario, an effective framework based on the intelligent algorithm solution is proposed. The novel EO algorithm is used to solve the dispatching scheme corresponding to the minimal comprehensive cost under different prediction error bounds. Finally, the effectiveness of the dispatching model and solution framework is verified under the improved IEEE-39 node.

The remainder of this paper is organized as follows. In section

The limited scenario can represent all error scenarios in the uncertainty set. The robust optimal dispatching model can meet the dispatching of all scenarios only by meeting all limited scenarios. In this section, the limited scenario method is used to establish the robust optimal dispatching model of wind fire energy storage system, and the objective function and related constraints are presented.

The limited scenario method is used to quantify the uncertainty of wind power prediction, and the established uncertainty set of wind power prediction is as follows:

The integrated energy system studied in this paper includes wind power, thermal power and AA-CAES. Among them, the mechanism model includes operation constraints and reserved capacity constraints. The cost models of these three types of power stations can be expressed in three parts: energy cost, environmental cost and standby market cost. As a part of the comprehensive cost, the production cost of the integrated energy system can be expressed as:

The power purchase cost, environmental cost and power purchase cost of thermal power, wind power and AA-CAES power stations are as follows:

In order to ensure the effectiveness and accuracy of the established dispatching model, some power system operation constraints need to be considered, including standby constraints, unit climbing constraints, wind power output constraints and wind power prediction uncertainty set constraints. The system operation constraints are set as follows:

• System power balance constraints and positive and negative reserved capacity constraints:

• Thermal power unit output upper and lower limit constraints, positive and negative reserved capacity climbing constraints, start and stop constraints:

• Wind power output constraints and wind power prediction uncertainty set constraints:

Baes on the established robust optimal dispatching model, the risk cost and comprehensive cost can be calculated. Risk cost includes the wind abandonment cost and load shedding cost. In this study, it is assumed that the probability distribution of wind power prediction is a normal distribution with the predicted value as the mean. Therefore, combined with the wind power probability distribution curve, the expected value of the wind power prediction range that fails to be absorbed by the dispatching plan can be obtained, which is the expected value of the abandoned wind power of the dispatching plan in this period. Meanwhile, the cost of wind abandonment also considers the fuel cost and environmental cost of thermal power. The expression of abandoned wind cost is as follows:

Meanwhile, the expected value is calculated by integrating the minimum wind power range that the system can dispatch, and then the expected value of abandoned wind power in the dispatching plan in this period can be obtained. Therefore, the load shedding cost can be expressed as:

Combined with the abandoned wind cost, load shedding cost and production cost, the comprehensive cost can be finally obtained as follows:

According to the definition of wind power prediction uncertainty set by the limited scenario method, the scaling factor

In order to solve the minimum comprehensive cost corresponding to the worst scenario under different

The solution framework for solving the robust optimal dispatching model.

Firstly, initialize

Use EO algorithm to solve the optimal scheduling scheme corresponding to the minimum comprehensive cost in the next 24 times when

First, input the initialized unit output value as the optimal output value at the previous time. Then, use EO algorithm to solve the dispatching model at present time moment, and get the corresponding production cost, risk cost and minimum comprehensive cost. When

According to the established objective function and the relevant constraints, the optimal dispatching model can be transformed into an optimization problem. In order to ensure the solution speed and accuracy, EO algorithm is presented to solve this optimization problem. EO algorithm is inspired by control volume mass balance models used to estimate both dynamic and equilibrium state. Considering the high-dimensional and multi constraint characteristics of this optimization problem, the intelligent algorithm solver proposed in this paper solves a single-step time series and takes the optimal scheduling output at the current time as the input of the next time, which can effectively reduce the solution time while ensuring the solution accuracy. The solver based on EO algorithm is structurally divided into the following parts:

Input the optimal dispatching output at the previous time and the system parameters. Determine the user defined parameters, number of design variables and their boundary conditions. Initialize the weight constant coefficient

Step 1: When the current iteration number

Step 2: Randomly select a candidate point in the equilibrium pool as

The optimal dispatching output at time

In order to prove the effectiveness of the established robust optimal dispatching model and the proposed solution framework, the improved IEEE-39 node based on a real power grid case is used. The cost changes of the system under different

Referring to a regional power grid system in China, the IEEE-39 node system is appropriately modified. Its structure diagram is shown in

Example node system diagram and wind farm output prediction and load curve:

The production cost, risk cost and minimum comprehensive cost of the whole system under different

The production cost, risk cost and minimum comprehensive cost under different

(%) |
Algorithm | Production cost [¥] | Risk cost [¥] | Minimum comprehensive cost [¥] |
---|---|---|---|---|

5 | EO | 12,648,207 | 10,646,596 | 23,294,803 |

GWO | 12,697,791 | 10,646,596 | 23,344,387 | |

10 | EO | 12,627,044 | 3,880,036 | 16,507,080 |

GWO | 12,692,989 | 3,880,036 | 16,573,025 | |

15 | EO | 12,617,752 | 1,379,707 | 13,997,459 |

GWO | 12,697,606 | 1,379,707 | 14,077,313 | |

20 | EO | 12,581,916 | 517,228 | 13,099,144 |

GWO | 12,655,983 | 517,228 | 13,173,211 | |

25 | EO | 12,546,433 | 219,318 | 12,765,750 |

GWO | 12,654,178 | 219,318 | 12,873,496 | |

30 | EO | 12,534,872 | 110,929 | 12,645,801 |

GWO | 12,673,696 | 110,929 | 12,784,625 |

When

Optimal scheduling scheme and corresponding minimum comprehensive cost:

This study has presented an effective intelligent algorithm solution framework for solving the established robust optimal dispatching model of wind fire energy storage which considering the application of AA-CAES and the uncertainty of wind power prediction. The simulation results show that the production cost and comprehensive cost can be further reduced by considering the uncertainty of wind power prediction. Meanwhile, the application of AA-CAES can increase the system flexibility and provide guarantee for system standby. On the other side, the equilibrium optimization algorithm shows better robustness and higher search accuracy than GWO in solving this optimization problem. In the future work, other uncertainties such as operation and maintenance uncertainty and energy storage location uncertainty will be further considered.

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

XC: Ideas; Development of methodology; Writing- Original draft preparation. LH: Methodology; Creation of models, Writing- Reviewing and Editing. XZ: Verification, Conceptualization, Supervision. SH: Validation, Data curation, Conceptualization, Supervision. ZS: Programming, revise grammar and correct expression. ZW: Programming, software development. TC: Specifically performing the experiments. SD: Conducting a research and investigation process.

This work was supported in part by the Plan for Major Provincial Science & Technology Project of Anhui Province under Grant 202003a05020019, and in part by the Comprehensive Research Facility for Fusion Technology under Grant 2018-000052-73-01-001228.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.