Despite significant economic and ecological effects, a higher level of renewable energy generation leads to increased uncertainty and variability in power injections, thus compromising grid reliability. In order to improve power grid security, we investigate a joint chance-constrained (CC) direct current (DC) optimal power flow (OPF) problem. The problem aims to find economically optimal power generation while guaranteeing that all power generation, line flows, and voltages simultaneously remain within their bounds with a pre-defined probability. Unfortunately, the problem is computationally intractable even if the distribution of renewables fluctuations is specified. Moreover, existing approximate solutions to the joint CC OPF problem are overly conservative, and therefore have less value for the operational practice. This paper proposes an importance sampling approach to the CC DC OPF problem, which yields better complexity and accuracy than current state-of-the-art methods. The algorithm efficiently reduces the number of scenarios by generating and using only the most important of them, thus enabling real-time solutions for test cases with up to several hundred buses.

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An automatic lab scale equipment for continuous HMF production by the dehydration of fructose with homogeneous catalysis in biphasic systems (water solution - MIBK) was designed, manufactured and tested. The dependencies of the HMF yield on the process parameters were studied. The feed rate of fructose varied from 50 to 100 g·h−1, the extractant (MIBK) flow rate from 0.5 to 1.4 L·h−1 and the process temperature was maintained in the range from 50 to 87 °C. The maximum productivity of the automatic lab scale equipment 33.6 g·h−1 with a high HMF yield 48 % is achieved with the following process parameters - 87 °C, the fructose feed rate 100 g·h−1 and the extractant flow rate 1 L·h−1. This automatic lab scale equipment may be prospectively used for the kilogram scale HMF synthesis.

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