Conclusions and Directions for Further Research
Because using many instruments, including pipelines, compressor stations, valves, and regulators over long distances and using a variety of network topologies and technologies, natural gas networks have been known as a complex and difficult problem to solve. Therefore, when this problem is mathematically modeled, the problem will be NP-hard and cannot be solved easily. On the other hand, gradient search and DP approaches have had limitations to consider real characteristics of network models because of their limitations in avoiding trapping a local optimality. From the optimization point of view, to solve planning models of natural gas networks, mathematical models and consequently metaheuristic algorithms seem the most desirable solution methods. This is more valuable when the problem is formulated based on transient assumptions and the cyclic topologies are considered. As it would appear from reviewing papers implemented in real cases, by optimal design of natural gas networks, which is possible using mathematical models and solving with suitable algorithms to find the closest optimum solution, considerable improvements can be achieved. Because of the enormity of the problem, even a small improvement in a natural gas network could save a huge amount of money per day, and the need to develop more models and algorithms is strongly felt among planners. Within optimization problems in natural gas networks, minimizing the fuel cost consumed by compressor stations has received more attention among researchers although there are not many developed models to optimize expansion or investment costs. To date, many optimization algorithms in different fields of natural gas networks planning have been introduced for all the steady state, the transient state, and different topologies, cyclic or noncyclic. Although the proposed optimization algorithms have been really successful, comprehensive models are needed to consider all constraints simultaneously and solve the problem aggregately. Moreover, in the developed models, transient systems have not been of interest to the researchers during the last decades to optimize because of increasing difficulties. In addition, cyclic topologies have had a few successes in researches and implementations. Because of difficulties available in natural gas networks planning, researchers usually avoid considering varieties in demand data, production data, and other fuzzy or statistical data. Therefore, it can be a suitable point for future researches to develop new models. Furthermore, a number of technical perspectives can make scientific gaps for new researches in the planning of natural gas networks, including considering the temperature as a neW variable, considering various types of compressor station units, and present- ing the network in low or medium pressures instead of high pressures only.
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