This paper is dedicated to the task of energy supply system planning in the market environment. The possibilities of forming coalitions between companies are considered. In this case, for justifying the decisions and distributing the gains a cooperative game theory is used. Methods of forming a coalition and distributing additional gains are described in this paper. The examples from heat supply planning and planning new network connections show the rationality and efficiency of using the considered approach.

This paper is dedicated to the task of cost determination for heat and power energy production for small size cogeneration plants. Cogeneration of heat and power is only possible if there is a demand for both types of energy. The consumers of both heat and power are considered as members of the coalition having a goal to gain additional revenues due to reduced investments and increased efficiency of power production. The suggested approach uses the cooperative game theory for distribution of the additional revenues between the members of the coalition. The examples from energy tariffs determination in the market environment show the rationality and efficiency of using the considered approach.

Paper presents power system protection and automation devices models validation methodology. Several model conformity criteria used for model validation. Validation procedure is carried out using the test signals obtained from power system regimes simulation. The out-of-step automation device model validation process is presented as an example o proposed methodology. Two simplified models are under consideration and pass the validation procedure.

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts.

Transforming energy supply systems is essential for achieving a sustainable, carbon-neutral future. However, this transition substantially increases the complexity of long-term planning due to the large-scale integration of carbon-free energy sources, strong interactions among technical and economic subsystems, multiple stakeholders, and significant uncertainty. Comprehensive evaluation of alternative development pathways requires detailed power system and electricity market simulations across numerous scenarios, resulting in a major computational bottleneck. The objective of this paper is to develop and demonstrate an artificial intelligence-based surrogate modelling framework that enables the rapid evaluation of large numbers of long-term power system development scenarios while preserving the accuracy of detailed simulation models. The proposed framework combines stochastic scenario generation, detailed power system and electricity market simulations, and artificial neural network (ANN)-based surrogate models to approximate scenario-dependent performance indicators. By replacing repeated high-fidelity simulations with ANN surrogates, the methodology substantially improves computational efficiency while supporting uncertainty analysis, multi-scenario assessment, and multi-stakeholder decision-making. A case study demonstrates the effectiveness of the proposed framework for scalable, high-resolution strategic power system planning.

This paper is dedicated to the task of cost determination for heat and power energy production for small size cogeneration plants. Cogeneration of heat and power is only possible if there is a demand for both types of energy. The consumers of both heat and power are considered as members of the coalition having a goal to gain additional revenues due to reduced investments and increased efficiency of power production. The suggested approach uses the cooperative game theory for distribution of the additional revenues between the members of the coalition. The examples from energy tariffs determination in the market environment show the rationality and efficiency of using the considered approach.