# Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage

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## Abstract

**:**

## 1. Introduction

- (1)
- The combined wind power and energy storage is applied as a flexible generator of the HES, which ensures more stable generation of the wind power based on the combined ESS.
- (2)
- Both system benefits and renewable energy waste are considered in the combined WPP-ESS based hybrid energy system (WEHES). Based on the cost calculation of CHP, wind power plant (WPP), PV, and ESS, a multi-objective hybrid energy system with the maximum benefits and minimum energy waste as the objectives is constructed. Further, the robust optimization and PSO algorithm are used to solve the proposed model.
- (3)
- In this paper, different scenarios are set up to analyze the economy of combined operation of the HES. By introducing demand response (DR) management to reduce the load fluctuation, combining with ESS, taking the connection type of ESS as one of the criteria of scenario division, three scenarios are set. The effects of applying ESS is analyzed, and so is the combined wind power and energy storage.

## 2. Structure of WEHES

## 3. WEHES Devices Model

#### 3.1. CHP Operation Model

#### 3.2. Wind Power Plant Generation Model

#### 3.3. PV Generation Model

#### 3.4. GB Operation Model

#### 3.5. Operation Loss of ESS

#### 3.6. Operation Cost of Combined WPP and ESS

## 4. WEHES Optimization Model

#### 4.1. Model Construction

- (1)
- maximum economic benefits

- (2)
- Minimum system energy waste

#### 4.2. Constraint Conditions

- (1)
- Electric power balancing

- (2)
- Thermal power balancing

- (3)
- CHP operation constraints

- (4)
- DR operation constraints

#### 4.3. Solving Method of the WEHES

- (1)
- Set size of the particle swarm and the maximum number of iterations. Input $\zeta $ , $r$ and $s$, and set accuracy.
- (2)
- The initial position information of each particle is given.
- (3)
- The extreme value of the individual particle and the particle swarm are obtained by fitness calculation.
- (4)
- Update the particle position. The next iteration begins.
- (5)
- When the number of iterations is exhausted or the result reaches the specified accuracy, the iteration ends and the optimal solution ${X}^{\prime}$ is obtained. At this time, the output composition of WEHES system can be obtained.

## 5. Case Study

#### 5.1. Basic Data

^{3}. The energy storage charging/discharging efficiency is 0.95. The self-discharge coefficient of the ESS is 0.01. The capacity of ESS is 5MW [28,29]. The number of particles is set to be 60. The iteration of PSO is 80 times. The initial velocity of the particles are random numbers of [0–1]. $\zeta $ decreases with the number of iterations from 0.9 to 0.4 [30,31,32]. In the calculation process, the iteration times setting as 80.

^{3}. The prices of WPP, PV, and CGT are 0.55 yuan/kWh, 0.75 yuan/kWh, and 0.45 yuan/kWh. After the implementation of DR, the price in normal period remains, with the price in peak period increasing by 10%, and the price in valley period decreasing by 20%.

#### 5.2. Optimization Results Analysis

- Scenario 1: Basic Scenario. DR is applied in this scenario, but no ESS.
- Scenario 2: ESS Scenario. ESS is accessed in the HES.
- Scenario 3: Combined WPP and ESS Scenario. The energy storage is combined with the wind power plant, and there is no other ESS in the system to prevent unnecessary cost.

#### 5.3. Result Analysis

- (1)
- Optimization results of scenario 1

- (2)
- Optimization results of scenario 2

- (3)
- Optimization results of scenario 3

- (4)
- Renewable energy consumption comparison

## 6. Conclusions

- (1)
- The ESS has significant contributions to the reduction of the total system cost. Based on the simulation results, by introducing the ESS, both the power curtailment costs, and the power purchasing costs reduce. By storing the excess wind power at the valley time and discharging at the peak time, more benefits can be gained.
- (2)
- The combination of WPP and ESS can improve the utilization of wind power to a greater extent and can directly absorb the excess wind power at the valley time and frequency adjustment while getting grid-connection. This combination is more suitable for the hybrid energy system with small proportion of PV power generation.
- (3)
- The storage and utilization of curtailed wind power is solved by proposed an optimization considering the uncertainty of the incoming wind and the flexible use of ESS, which can be used in the actual situation. However, research on the fluctuation of load side is not profound, which will become a focus of our future research.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Conflicts of Interest

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Types | Time Periods | Power Price (Yuan/kWh) |
---|---|---|

Peak periods | 10:00–15:00 and 18:00–21:00 | 1.25 |

Valley periods | 00:00–07:00 and 23:00–24:00 | 0.49 |

Flat periods | the rest time periods | 0.86 |

Scenarios | WPP | PV | CHP | Power Purchasing |
---|---|---|---|---|

Scenario 1 | 71.658 | 8.6 | 86.25 | 6.78 |

Scenario 2 | 78.726 | 8.846 | 77.4 | 3.227 |

Scenario 3 | 79.206 | 8.846 | 78.894 | 0.799 |

Index Comparing | Scenario 1 | Scenario 2 | Scenario 3 |
---|---|---|---|

Calculated system benefits (yuan) | 83,128.68 | 104,375.2 | 107,546.7 |

Mean value of system benefits (yuan) | 83,167.73 | 104,299.4 | 107,621.0 |

Calculated power curtailment (MWh) | 12.27 | 2.554 | 2.507 |

Mean power curtailment (MWh) | 12.29 | 2.549 | 2.511 |

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**MDPI and ACS Style**

Wu, J.; Tan, Z.; Wang, K.; Liang, Y.; Zhou, J.
Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage. *Sustainability* **2021**, *13*, 3098.
https://doi.org/10.3390/su13063098

**AMA Style**

Wu J, Tan Z, Wang K, Liang Y, Zhou J.
Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage. *Sustainability*. 2021; 13(6):3098.
https://doi.org/10.3390/su13063098

**Chicago/Turabian Style**

Wu, Jing, Zhongfu Tan, Keke Wang, Yi Liang, and Jinghan Zhou.
2021. "Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage" *Sustainability* 13, no. 6: 3098.
https://doi.org/10.3390/su13063098