Open AccessDynamic stochastic joint expansion planning of power systems, natural gas networks, and electrical and natural gas storage
Author(s) -
Gholami Arash,
Nafisi Hamed,
Askarian Abyaneh Hossein,
Jahanbani Ardakani Ali
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12277
Subject(s) - natural gas , power to gas , electricity , electric power system , scalability , node (physics) , computer science , electric power , power (physics) , engineering , electrical engineering , waste management , chemistry , physics , electrolysis , structural engineering , electrode , quantum mechanics , database , electrolyte
Over the last decades, electricity generation from natural gas has substantially increased, mostly driven by low natural gas prices due to fracturing and lower extraction costs. The geographic distance between natural gas resources and load centers calls for a holistic tool for joint expansion of power systems and natural gas networks. In this paper, a Dynamic Stochastic Joint Expansion Planning (DSJEP) of power systems and natural gas networks is proposed to minimize the investment and operational costs of power and natural gas systems. Electrical and natural gas storage (ENGS) are considered as an option for decision‐makers in the DSJEP problem. The proposed approach takes into account long‐term uncertainties in natural gas prices and electric and natural gas demands through scenario realizations. In dynamic planning, more scenario needs more time for computation; therefore, scenario reduction is implemented to eschew unnecessary scenarios. The proposed formulation is implemented on a four‐bus electricity system with a five‐node natural gas network. To demonstrate the efficiency and scalability of the proposed approach, it is also tested on the IEEE 118‐bus system with a 14‐node natural gas network. The numerical results demonstrate that ENGS can reduce the total investment cost, up to 52% in the test cases, and operational cost, up to 3%. In this paper, co‐planning of power and natural gas systems considering natural gas and electrical storage is represented. Also, electrical and natural gas load growth uncertainties are taken into account to model the real situations. The purpose of the model is to minimize investing and operational costs.