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Microgrids can be used for securing the supply of power during network outages. Underground cabling of distribution networks is another effective but conventional and expensive alternative to enhance the reliability of the power supply. This paper first presents an analysis method for the determination of microgrid power supply adequacy during islanded operation and, second, presents a comparison method for the overall cost calculation of microgrids versus underground cabling. The microgrid power adequacy during a rather long network outage is required in order to indicate high level of reliability of the supply. The overall cost calculation considers the economic benefits and costs incurred, combined for both the distribution network company and the consumer. Whereas the microgrid setup determines the islanded-operation power adequacy and thus the reliability of the supply, the economic feasibility results from the normal operations and services. The methods are illustrated by two typical, and even critical, case studies in rural distribution networks: an electric-heated detached house and a dairy farm. These case studies show that even in the case of a single consumer, a microgrid option could be more economical than network renovation by underground cabling of a branch in order to increase the reliability.The profitability possibilities of residential microgrids as an aggregator-based solution to the perspective of different stakeholders, for example, utilities, aggregators, and prosumers, were analyzed in [6]. The feasibility and profitability of microgrids participating in the primary frequency control reserve (FCR) market through an aggregator were assessed in [7]. Furthermore, battery energy storage system (BESS) usage on the frequency regulation market was analyzed in [8].According to [9], underground cabling of the network is an effective way for distribution system operators (DSOs) to increase the reliability of power supply. However, underground cabling is expensive.Today, farming is highly automated and electricity-dependent [10], and even short power interruptions are very detrimental. Farming is an energy-intensive industry [11], and thus farmers value the reliability of the electricity supply more so than most of the other customer groups.Farms are located naturally in rural areas, possibly on the long distribution network radial branches with low electricity customer density. The majority of farmers have backup generators (e.g., [11]). Farmers having their own power production to cover a portion of their electricity need is gaining popularity.Several recent studies have focused on microgrid islanded-mode operation, microgrid energy management systems, and power supply adequacy and forecasting (e.g., [12][13][14]). The power supply capability in islanded-mode operation was assessed in [12] over a few hours by using a simulation maximum time step of 1 min. Electro-technical aspects of an unexpected microgrid islanded operation were also analyzed in [14] while considering optimal energy management of...

Autonomous mobile machines are applied increasingly often in outdoor applications, but only in places, where logistic problems and safety issues are resolvable. Solving safety issues require good hazard identification and risk estimation. The aim of this study is to show which new risks emerge in autonomous mobile machines compared to manual machines. There are new risks, which are valid only in autonomous mobile machines (e.g., related to sensors for situational awareness), and risks, which exist in both machine types, but human presence can make the difference in resolving the situation. Humans can take sophisticated protective measures during an unexpected hazardous situation. Another aim of this work is to give information to designers and assessors to consider risks of their own design. Autonomous mobile machines are complex, and their performance is not always easily predictable. A checklist of risks would help an assessor, but currently, it may be difficult to find a checklist for a specific autonomous mobile machine. The main result of this study is a checklist of new risk sources or hazards of autonomous mobile machines.

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