Conference Agenda

This paper focuses on a methodology developed to evaluate innovative control approaches for optimizing energy management in the context of energy flexibility, rising energy consumption, and thermal discomfort. To allow the development and testing of this evaluation methodology, various architectures of Model Predictive Controllers, which are renowned for their ability to address these challenges effectively, have been implemented. To assess the impact of predictive control on energy systems management optimization, the methodology is based on a multi-objective set of KPIs. Therefore, several performance indicators are defined and implemented to evaluate the controllers' effectiveness. Evaluations are carried out by integrating the controllers with a bottom-up dynamic simulation platform dedicated to district energy calculations, as an emulation tool. This offers, at last, an architecture and a methodology for comparing the performances of controllers, especially model predictive controllers.

Recent studies in Italy have shown that Renewable Energy Communities (RECs) can provide a viable and economically beneficial alternative to traditional energy supply. In addition, the 2019 EU directive mandates that the share of energy from renewable sources will be at least 32% by 2030. As Hungary is currently in the process of developing REC regulations, this work aims to devise an optimal energy management scheme for RECs, tailored to the Hungarian economic and infrastructural environment. The scheme is based on an optimal energy management framework utilizing mixed integer linear programming (MILP) to control a community energy storage system. The scheme is extended to take into account the unique consumption patterns in Hungary, especially the high penetration of controlled loads. In Hungary, controlled loads are managed by the electricity provider through demand-side management techniques, allowing partial control over their operation to effectively manage peak hours. This article highlights the benefits of utilizing load control in REC energy management, increasing the flexibility of the REC and as a result, leading to reduced optimal community battery size. To assess the results, a case study of four low-voltage Hungarian transformer areas is conducted. The optimal ratio of photovoltaic penetration and community battery size is determined, and economic key performance indicators are evaluated to assess the financial viability of these communities.

This talk analyzes different optimization models for evaluating investments in Energy Storage Systems (ESS) in power systems with high penetration of Renewable Energy Sources (RES). First of all, two methodologies proposed in the literature are extended. The enhanced models are the ‘System States Reduced Frequency Matrix' model, and the ‘Enhance Representative Periods’ model which guarantees some continuity between representative periods, e.g. days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as a benchmark. While the system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The enhanced representative period model, on the other hand, succeeds in approximating both short- and long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.

The charging of electrical vehicles is often limited by electrical grid infrastructural constraints: the larger the number of electrical vehicles the higher the value of electrical energy needed to charge them and, in a given time interval, the power requested to the grid. An optimal strategy can distribute over time the charging sessions so that the grid peak power is reduced. The present work defines the constraints of an electrical bus charging system and presents two optimization strategies: one based on heuristic strategy and another on Mixed Integer Linear Programming. The two strategies can be used alternatively or in sequence: the first providing a starting point for the second. The optimal procedures are applied to the case of charging system for a fleet of electrical buses and some preliminary results are presented.