The number of electric vehicles (EV) in the world has been increasing and is gaining momentum. The large-scale use of EVs in public life has initiated the need to establish EV battery charging services within the power system. Currently, EVs serve as a transportation tool and also as a flexible load. This publication examines the possibility of the owner of an electric vehicle choosing a battery recharging point, as well as of the involvement of several decision makers in the selection of a charging schedule. This problem is important because we assume that a significant proportion of EVs mainly use two parking spaces, one located close to the place of residence and another close to the workplace. We accept and prove that a car charging station can be created by the employer (company) and implemented in the best interests of the employer and the employee (EV owner). For that, a coalition between the company and the EV owner has to be formed. To support rational decisions, this study solves the problem using the cooperative game theory and designs a payment distribution mechanism based on the Shapley value. The results obtained prove that the coalition is beneficial under different conditions, which depend on the capacity of the EV, the distance between the workplace and the place of residence, the difference in the electricity prices of the day, as well as the consumption of the company. In order to estimate the coalition’s gain, it is necessary to take into account the structure of the power tariff system for both the company and the EV owner. Furthermore, we prove that the presence of a coalition allows the company and the EV owner to reduce the annual fee for consumed power. The results of this analysis could be adopted by decision makers such as government agencies, companies, EV owners, and they are recommended for potential investors for the development of transport electrification and smart energy.

Reservoir inflow forecasting with artificial neural networks is presented in this paper. Different types of ANN input data were considered such as temperature, precipitation and historical water inflow. Performance of the hourly inflow forecasts was assessed based on a case study of a specific hydropower reservoir in Latvia. The results showed that all the approaches had similar prediction errors implying that for optimal hydropower scheduling uncertainties need to be modelled which is also proposed in this study through generation of several forecast realisations in addition to point predictions.

The paper discusses the planning of hydroelectric power generation. A stochastic optimization procedure is offered to solve the complex task of planning the operation of three hydroelectric power plants. The proposed stochastic optimization algorithm is based on time average revenue maximization, taking into account the random nature of the future energy prices and river water inflows. Random variables are predicted by using an algorithm based on artificial neural networks. For computing within the operational planning, the task is divided into three parts. First, middle-term planning is used to solve the water resources distribution task. The second and third parts are related to day-ahead operational and unit commitment planning; in those cases, nonlinear programming tools are used. A case study presents the results of electric power generation in the case of optimum water resource distribution in the storage basins of hydroelectric power plant's cascade. The paper proves the workability of the developed algorithm for maximizing the income value and is intended to enable and support improved planning and decision-making for electric power producers.

This study explores the challenges posed by the transition to large-scale deployment of renewable energy sources, which lead to a significant reduction in the inertia of power systems. It focuses on enhancing resilience and frequency stability in response to sudden disturbances, such as generator failures or transmission line disconnections. The paper critically evaluates the limitations of conventional under-frequency load shedding (UFLS) methods and advances a novel Rapid Load Shedding (RLS) approach. The RLS method harnesses the active power response of synchronous condensers (SCs) to enable faster load shedding, thereby improving system stability and addressing operational constraints from a reliability perspective. The study demonstrates the feasibility of implementing new control systems using the proposed structure of a special protection scheme. It also examines the economic benefits of enhancing the allowable transfer capacity in the Baltic Power System (BPS) through this method. The findings highlight how the RLS approach can enable higher transfer capacities whilst maintaining system resilience. Additionally, the results indicate that the RLS method can significantly mitigate frequency deviations and enhance system stability, offering a promising solution for low-inertia power systems with high levels of renewable energy integration.

The synthesis of methane from hydrogen and carbon dioxide creates an energy resource that is suitable for long-term storage. Once this process is powered by renewable electricity, it produces a clean fuel for producing electricity and heat and supports large-scale renewable energy deployment, energy transition and climate change mitigation. This paper proposes a pragmatic approach to assessing the economic potential of synthetic methane-based power. Today, natural gas plays an important role in the Baltic region due to the existing infrastructure, which includes a transmission and distribution pipeline network, gas power plants and a large underground storage reservoir. Replacing natural gas with synthetic methane would fulfil carbon emission reduction ambitions. In this paper, we simulate electricity producers’ actions at market conditions and consider the generation portfolio in the Baltics and the interconnections with Scandinavia and Poland operating in the NORDPOOL electricity market. As a result of these calculations, we obtain the volume of the synthetic gas, the production costs, the volume of gas storage, the installed capacity of the gas power plant, and the investments required to ensure energy transition and system adequacy. These results are essential for the informed decisions made by policymakers, investors and system operators.

The paper describes possibility to apply innovation technology of Smart Grid for power system emergency automation. Operational characteristics of existing emergency automation and new suggested one are compared in the paper. The method for liquidation of drawback of existing automation is suggested. The paper describes the developed mathematical model of intellectual underfrequency load shedding system and its operational algorithms.