A probabilistic demand side management approach by consumption admission control

Nova generacija električne mreže pod nazivom pametna mreža (Smart Grid) je nedavno zamišljena vizija čišćeg, učinkovitijeg i jeftinijeg elektroenergetskog sustava. Jedan od najvećih izazova električne mreže je da bi proizvodnja i potrošnja trebale biti uravnotežene u svakome trenutku. U radu se uvodi novi koncept za kontrolu potrošnje sredstvima automatski omogućavanih/onemogućavanih električnih aparata kako bi bili sigurni da je potrošnja usklađena s raspoloživim zalihama, na temelju statističkih karakterizacija potreba. U našem novom pristupu, umjesto uporabe tvrdih granica procjenjujemo vjerojatnost kraja distribucije potrošnje i sustava kontrole pomoću načela i rezultata statističkog upravljanja resursima.New generation electricity network called Smart Grid is a recently conceived vision for a cleaner, more efficient and cheaper electricity system. One of the major challenges of electricity network is that generation and consumption should be balanced at every moment. This paper introduces a new concept for controlling the demand side by the means of automatically enabling/disabling electric appliances to make sure that the demand is in match with the available supplies, based on the statistical characterization of the need. In our new approach instead of using hard limits we estimate the tail probability of the demand distribution and control system by using the principles and the results of statistical resource management

Automatic topology identification of weak low voltage networks and load management strategies for micro-mobility applications

With 55 % of the world's population residing in urban areas in 2018 and a projected rise up to 68% by 2050 [1], the challenge of integrating sustainable mobility solutions into the existing urban infrastructure is gaining worldwide attention. The new opportunities come with a challenge, which is focused on managing a dynamic combination of generations and loads on an existing infrastructure that is designed based on a set of particular standards and specification to support its static original conditions. Networks reinforcement is one solution, however this solution is expensive and typical usage times are short. An alternative is to integrate smart grid control techniques, avoiding relatively larger investments. For this purpose, an energy management system retrofitted to an existing public street lighting network can provide a more economic and reliable solution. In this work a systematic approach to optimizing the scheduling of loads and power flows in terms of maximizing load acceptance rate and the total delivered energy is presented. 1.H. Ritchie and M. Roser, "Urbanization", Our World in Data, 2020, [online] Available: https://ourworldindata.org/urbanization

Microload Management in Generation Constrained Power Systems

The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction