Roman Petrichenko

The paper presents an integrated approach to Optimal Power Flow (OPF) in transmission systems with a high share of renewable energy sources (RESs) supported by energy storage systems (ESSs). Unlike conventional approaches, the proposed integrated evaluation framework minimizes operating cost subject to technical (network and ESS) constraints, while separately reporting RES utilization/curtailment and CO₂ outcomes as post-solve indicators. The model was implemented as a browser-based JavaScript application using a custom Big-M Simplex linear programming solver and tested on a 24 h horizon under multiple scenarios representing conditions informed by Baltic/Latvian load, generation, and price profiles, applied to a modified IEEE 9-bus test network, including standard operation, high price volatility, high RES penetration, and a transmission line outage (N-1 criterion). The results demonstrate that ESS integration together with RES usage maximization reduces total operating costs by up to 10.2% compared to the baseline strategy without ESS and optimization (about 4.7 percentage points of this saving come from the lower minimum output of the conventional unit alone), increases RES share by about 5 percentage points, and decreases RES curtailment to 0%, under a terminal state of charge condition that prevents the ESS from ending the horizon more depleted than it started (imposed in HiGHS runs of the same linear program). Under high RES scenarios, ESSs become essential for balancing variability, while in volatile markets they support stable dispatching within the model. A sensitivity analysis highlights the dominant influence of market price fluctuations, with smaller effects of ESS capacity and RES share on total operating cost. The findings indicate that, within the transportation-type network model used, the proposed approach improves economic efficiency and environmental performance, offering a practical decision-support tool for transmission system operators and energy planners.

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.

Transforming energy supply systems is vital for achieving a sustainable, carbon-neutral future. However, this transformation entails significant complexity, driven by the integration of carbon-free energy sources, tightly coupled technical and economic subsystems, and the need to ensure reliability, resilience, and affordability. Addressing these challenges requires advanced planning approaches capable of capturing uncertainty, multiple stakeholder perspectives, and system dynamics across temporal and spatial scales. In response, this article presents an integrated planning and operational framework that combines multi-actor, multi-criteria, and multi-stage stochastic optimization with surrogate modelling and artificial intelligence. The proposed approach enhances scalability, computational efficiency, and precision in the planning and operation of future energy systems. A case study illustrates its effectiveness in supporting high-resolution, uncertainty-aware decision-making.

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets.

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.

Generator maintenance scheduling presents many engineering issues that provide power system personnel with a variety of challenges, and one can hardly afford to neglect these engineering issues in the future. Additionally, there is vital need for further development of the repair planning task complexity in order to take into account the vast majority of power flow constraints. At present, the question still remains as to which approach is the simplest and most effective, as well as appropriate for further application in the power flow-oriented statement of the repair planning problem. This research compared directed search, differential evolution, and very fast simulated annealing methods based on a number of numerical calculations and made conclusions about their prospective utilization in terms of a more complicated mathematical formulation of the repair planning task. A comparison of results shows that the effectiveness of directed search methods should not be underestimated, and that the pure differential evolution and very fast simulated annealing approaches are not essentially reliable for repair planning. The experimental results demonstrate the perspectivity of unifying single-procedure methods in order to net out risk associated with specific features of these approaches.

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.