Published June 2019 | Version v1
Journal article

Power supply chain network design problem for smart grid considering differential pricing and buy-back policies

  • 1. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei (China)

Description

Highlights: • Addresses the smart power supply chain network design problem • Consider two decision makers: the electric power company and the users • Consider differential pricing and buy-back policies • Continuous approximation approach is used to model the problem • The results can serve as references for business managers or administrators. -- Abstract: With the rising sense of environmental consciousness, the development of renewable energy and rapid technological innovation have become drivers, as well as posed challenges, for the power supply chain network. This study addresses the smart power supply chain network design problem considering two players: the electric power company and the users. Using distributed generations (DGs) such as solar, wind, and biomass, among others, users can generate their own renewable energy. Here, differential pricing and buy-back policies maximize benefits for both the companies and users. Under the buy-back contract, users who own DGs can generate renewable electricity, determine the electricity they need, and buy from or sell their share to the electric company. The continuous approximation approach is used to model resolutions for smart power supply chain network problems. Algorithms based on non-linear optimization are proposed to solve the smart power supply chain network design problems for two cases: centralized and decentralized models. Finally, a numerical analysis illustrates the solution procedures and examines the effects of dynamic parameters on decision-making. The results show that the centralized model obtains a higher profit than the decentralized model. Further, the results of the numerical analysis can serve as references for business managers or administrators.

Additional details

Identifiers

DOI
10.1016/j.eneco.2019.04.022;
PII
S0140988319301367;

Publishing Information

Journal Title
Energy Economics
Journal Volume
81
Journal Page Range
p. 493-502
ISSN
0140-9883
CODEN
EECODR

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
55014698
Subject category
S29: ENERGY PLANNING, POLICY AND ECONOMY;
Descriptors DEI
ALGORITHMS; DECISION MAKING; DESIGN; ELECTRIC POWER; ELECTRICITY; ENERGY POLICY; NONLINEAR PROBLEMS; NUMERICAL ANALYSIS; OPTIMIZATION; PROFITS; SMART GRIDS
Descriptors DEC
ENERGY SYSTEMS; GOVERNMENT POLICIES; MATHEMATICAL LOGIC; MATHEMATICS; POWER; POWER SYSTEMS

Optional Information

Copyright
Copyright (c) 2019 Elsevier B.V. All rights reserved.