The purpose of this study was to analyze the economical suitability of numerous on-site renewable electricity generation technologies which were intended to be used in a recently built industrial facility designed and utilized as a warehouse. The facility was located in the vicinity of Riga, Latvia. Data were collected and calculations were performed within the scope of the project “Mitigating Energy Poverty through Innovative Solutions” as part of several planned activities to address the broad spectrum of energy poverty and self-reliance issues in both the residential sector and small-scale industrial facilities. During the project, evaluations of various renewable energy technologies, including PV installations, wind energy installations, battery storage solutions, and hybrid technologies, were carried out. The aim of these evaluations was to develop an electricity production–consumption model for efficient and cost-effective energy use and to reduce greenhouse gas emissions from the test facility. A model was created and subsequent research scenarios were developed based on a payback period instead of the net present value criterion. The project was carried out over several steps to develop a calculation methodology. The open access databases of energy resource providers were used to evaluate statistical data and make forecasts for the analysis of the electricity consumption of companies. MATLAB/Simulink 23/2 was used for the data analysis, and the H-TEC method was employed. This made it possible to modulate the required production capacity as the model allowed for the addition of new modules to modules already installed. The project results proved that despite high initial investment costs, renewable energy sources and efficient storage systems can provide cost-effective solutions and reduce dependence on fossil fuels in the long term.

Active customers play a critical role in the successful implementation of support schemes, paving the way for the emergence of an energy community. This analysis explores the cooperation among active customers and the implications for developing energy communities. Furthermore, the motivations for consumers becoming active customers in the context of Latvia are illuminated, while also exploring the broader context of navigating the complex regulatory landscape to promote self-consumption. In contrast to prior studies, which often focus on individual or homogenous group participation, this analysis uniquely examines collaborative frameworks that incorporate varied customer categories and profiles. This approach not only underscores the role of tailored regulatory structures in fostering self-consumption, but also presents practical policy insights for incentivizing community-based energy models. The findings reveal that individual participation of active customers in support schemes only achieves the minimal self-consumption threshold in 47% of cases. In contrast, membership in an energy community significantly increases this rate, reaching 84%. These encouraging results underscore the importance of promoting energy community membership among active customers, which subsequently demonstrates substantial potential when promoted across diverse load profile categories. Additionally, the integration of photovoltaic and wind turbine technologies consistently improves self-consumption values.

The article presents an operation planning optimisation approach using a genetic algorithm for CHPPs in an urban heat supply zone. Changes in market environment will result in a significant change in energy system planning and operation. For efficient production planning in the liberalized power markets, it is paramount to determine the optimal strategies over a time horizon. Solution of an optimization task of such complexity requires a well-crafted set of tailored modelling, simulation, optimization, and forecasting tools. A well-designed solution is unthinkable without a great number of production units, such as gas turbines, steam turbines, heat-only boilers, and thermal storages, reflected by the optimization model.

This paper presents the development of micro-processor based device with ability to decrease protection-type current transformer measurement error in the area of small currents. Measurement transformers are available with a high precision class of 0.2 and 0.5 and maximal measurable current up to double rated value. Transformers, produced for relay protection purposes, are with error classes of 3, 5 and 10 meaning error rates of 3, 5 and 10 % respectively. Our idea consists in a new way for joining the characteristic curves of relay-type and measurement-type current transformers. The micro-processor based device may “teach” itself by forming some error correction table in it's memory on the basis of core outputs compare during normal operation. In case of fault device may operate with much higher sensitivity thus sensing fault on it's early stage.

This paper presents the development of micro-processor based device for improving the sensitivity of differential protection of power transformers. Power transformers failure may lead to high scale system's operations disruption and heavy economic losses, that's why the lever of requirements for power transformer protections is so high. Differential protection is usually used as a main protection against internal faults such as inter-windings faults, inter-coil faults or coil-core faults. For safe protection against incomplete winding faults protection device must be capable to detect differential current from such a very low value from nominal. The idea for a new device is to use information extracted from inrush current thru Fourier transformation to perform an early detection of faults usually hardly detectable due to very low impact on transformer's primary current in steady condition. In case of fault device may operate with much higher sensitivity thus sensing fault on its early stage.

Using combined heat and power plants (CHPP) is widespread in the Nordic countries, where district heating as a thermal load for CHPPs is typical for cities and towns. However, CHPP operation becomes more complicated at power market conditions. Errors in the forecasting of the temperature, which lies at the basis of CHPP operation mode planning (the difference between forecasted heat demand and the real one leads to a difference between planned and real electricity production), lead to an imbalance in electricity production in the day-ahead market. To improve CHPP profitability at power market conditions, different technologies are used to increase CHPP operation flexibility. The overall objective of this study is to evaluate, based on the example of the city of Riga, what possibilities of flexibility for each hour can be provided by the inertia of the heating system of Riga at possible temperature deviations from the comfort temperature.