This paper aimed at assessing the technical and economic potential of using rooftop solar photovoltaic (PV) systems in Lithuanian urban areas to support energy and climate policy formation and its implementation in the country. A bottom-up approach was applied. A number of apartment (AP), commercial (COM) and public (PUB) buildings, electric vehicle (EV) charging infrastructures and solar PV systems in the largest Lithuanian cities were analyzed, and the results were tailored to national-wide estimates. LiDAR and photogrammetry data of building topologies, geographical information systems (GIS), digital maps for rooftop measurements, factual solar photovoltaic (PV) performance modeling, average profiles of solar PV systems, statistical data of energy consumption in buildings, levelized cost of energy (LCOE) models and the literature review method were used. The results of the assessment of selected buildings and their categories reveal that the rooftop area per installed PV unit was 14.1–18.3 m²/kW in AP buildings, followed by 18.0–18.6 m²/kW in COM and 6.1–18.3 m²/kW in PUB buildings; the factor of rooftop suitability for solar PV system installation varied in ranges of 31.5–85.4%, 48.9–99.4% and 50.1–88.2%, respectively. Corrected by expert judgement, these estimates served as inputs for the assessment of technical and economic potentials. Furthermore, energy generated by solar PV could be consumed locally with little supplied to the distribution grid, resulting in no or a limited need for expensive investments in infrastructure. In addition, locating an EV charger on the premises of COM or PUB buildings could improve the localized consumption of solar energy. Thus, rooftop solar PV systems in urban Lithuanian areas are preferable. The results of the technical potential analysis show that, on the rooftops of AP buildings, 272 MW could be installed, followed by 78 MW on roofs of PUB and 67 MW on COM buildings. If technical potential is extracted, EUR 332.7 million can be invested. The cost of energy produced by solar PV systems was estimated at 2.34–5.25 EURct/kWh, which is significantly lower than the prices of market and retail electricity, standing at 23 EURct/kWh and 24 EURct/kWh (with support from the government) in 2022, respectively. The research’s limitations are defined by the fact that average values were applied to estimate the technical and economic potential of rooftop solar PV systems; a limited number of building categories were analyzed; the category of private houses, which is minor in cities, was missed, and rural areas with a great number of private houses were not covered by this study.

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.