면역알고리즘 기반의 MECs (에너지 허브) 시스템 = An Immune Algorithm based Multiple Energy Carriers System

다국어 초록 (Multilingual Abstract)

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 면역 알고리즘
• 3. 면역알고리즘 기반의 MECs(에너지 허브) 시스템
• 4. 결론
• Reference

참고문헌 (Reference)

1 서우석, "스마트그리드(Smart Grid) 전력망과 정보통신망 융합 보안 방향" 한국전자통신학회 05 (05): 477-486, 2010

2 조재훈, "면역 알고리즘의 개선된 클론선택에 의한 퍼지 뉴로 네트워크와 교통경로선택으로의 응용" 한국지능시스템학회 14 (14): 402-410, 2004

3 U. Aickelin, "Search Methodologies : Introductory Tutorials in Optimization and Decision Support Techniques" 2008

4 Payman Dehghanian, "Optimal siting of DG units in power systems form a probabilistic multi-objective optimization perspective" 51 : 14-26, 2013

5 Krause, T., "Multiple-Energy Carriers : Modeling of Production, Delivery, and Consumption" 99 (99): 15-27, 2011

6 NAHUM SHIMKIN, "LEARNING IN COMPLEX SYSTEMS LECTURE NOTES" 1-16, 2011

7 Moeini-Aghtatie, M.., "Fotuhi-Firuzabad and M. Abbaspour, Mulitagent Genetic Algorithm : An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability" 5 (5): 699-708, 2014

8 Givoni, Baruch, "Climate Considerations in Building and Urban Design" Van Nostrand Reinhold 1998

9 Ashrae, "ASHRAE Handbook 1993 Fundamentals" ASHRAE 1993