Aleksandrs Sahnovskis

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.

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.