DOI:10.1007/s40565-017-0291-2 |
| |
| |
Economic optimization for configuration and sizing of micro integrated energy systems |
| |
|
| |
Page view: 0
Net amount: 950 |
| |
Author:
Haibo YU1,2, Chao ZHANG1,2, Zuqiang DENG1,2, Haifeng BIAN1,2, Chenjun SUN3, Chen JIA1,2
|
Author Affiliation:
1. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China; 2. Beijing NARI Dianyan Huayuan Electric Power Technology Co. Ltd., Beijing 102200, China; 3. State Grid Hebei Electric Power Company, Shijiazhuang 050021, China
|
Foundation: |
This work was supported by the Science and Technology Project of State Grid Corporation of China (No. 52467K150007). |
|
|
Abstract: |
Based on analysis of construction and operation of micro integrated energy systems (MIES), this paper presents economic optimization for their con?guration and sizing. After presenting typical models for MIES, a residential community MIES is developed by analyzing residential direct energy consumption within a general design procedure. Integrating with available current technologies and local resources, the systematic design considers a prime mover, fed by natural gas, with wind power, photovoltaic generation, and two storage devices serving thermal energy and power to satisfy cooling, heating and electricity demands. Control strategies for MIES also are presented in this study. Multi-objective formulas are obtained by analyzing annual cost and dumped renewable energy to achieve optimal coordination of energy supply and demand. According to historical load data and the probability distribution of distributed generation output, clustering methods based on K-means and discretization methods are employed to obtain typical scenarios representative of uncertainties. The modi?ed non-dominated sorting genetic algorithm is applied to ?nd the Pareto frontier of the constructed multi-objective formulas. In addition, aiming to explore the Pareto frontier, the dumped energy cost ratio is de?ned to check the energy balance in different MIES designs and provide decision support for the investors. Finally, simulations and comparision show the appropriateness of the developed model and the applicability of the adopted optimization algorithm. |
Keywords: |
Micro integrated energy system (MIES), Economic optimization, Uncertainties, Non-dominated sorting genetic algorithm (NSGA-II) |
| |
Online Time:2018/03/20 |
| |
|
|
View Full Text
Download reader
|
|
|