Natural gas supply chain under uncertainty condition

In today’s competitive world, uncertainty is an integral part of all optimization problems. One of the cases where uncertainty has the greatest impact on optimization issues is SCN design. In most of the conducted studies, parameters such as demand, transportation cost and capacity of tehsils have been published in an uncertain manner. In this type of problem, various methods have been used to control these uncertainty parameters, which can be referred to as fuzzy programming, robust optimization, two-stage stochastic programming, multi-stage stochastic programming, multi-stage fuzzy stochastic programming, fuzzy robust optimization. Each of the mentioned methods has limitations in terms of its implementation. in the fuzzy programming method, there is no deviation from the data collected by experts’ opinions. In probabilistic methods, it is very difficult to determine the exact type of distribution function. Therefore, many researchers have investigated the strengths and weaknesses of each method in their studies. Therefore, in this paper we try to review the strength and weakness of these methods to apply the best approaches for different situations. In this paper, a real-world case study of a natural gas supply chain is investigated. By using concepts related to natural gas industry and the relations among the components of transmission and distribution network, a Five-level supply chain has been introduced and presented schematically

Risk Hedging Strategies in New Energy Markets

In recent years, two typical developments have been witnessed in the energy market. On the one hand, the penetration of renewable generations has gradually replaced parts of the traditional ways to generate energy. The intermittent nature of renewable generation can lead to energy supply uncertainty, which might exacerbate the imbalance between energy supply and demand. As a result, the problem of energy price risks might occur. On the other hand, with the introduction of distributed energy resources (DERs), new categories of markets besides traditional wholesale and retail markets are emerging. The main benefits of the penetration of DERs are threefold. First, DERs can increase power system reliability. Second, the cost of transmission can be reduced. Third, end users can directly participate in some of these new types of markets according to their energy demand, excess energy, and cost function without third-party intervention. However, energy market participants might encounter various types of uncertainties. Therefore, it is necessary to develop proper risk-hedging strategies for different energy market participants in emerging new markets. Thus, we propose risk-hedging strategies that can be used to guide various market participants to hedge risks and enhance utilities in the new energy market. These participants can be categorized into the supply side and demand side. Regarding the wide range of hedging tools analyzed in this thesis, four main types of hedging strategies are developed, including the application of ESS, financial tools, DR management, and pricing strategy. Several benchmark test systems have been applied to demonstrate the effectiveness of the proposed risk-hedging strategies. Comparative studies of existing risk hedging approaches in the literature, where applicable, have also been conducted. The real applicability of the proposed approach has been verified by simulation results