Analysis of the potential for PV rooftop prosumer production: Technical, economic and environmental assessment for the city of Valencia (Spain)

Gómez‐Navarro, Tomás; Brazzini, Tommaso; Alfonso-Solar, David; Vargas‐Salgado, Carlos

doi:10.1016/j.renene.2021.04.049

Cited by 65 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On-site decentralized renewable energy generation is believed to be advantageous to a city's energy resilience as reliance on external factors such as unreliability in outer supply are reduced ( Gómez-Navarro et al, 2021 ). With the aid of optimal weather conditions, for instance solar irradiance, photovoltaic renewable energy system is capable of delivering reliable performance by maintaining steady output ( Acuña et al, 2017 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Photovoltaic rooftop's contribution to improve building-level energy resilience during COVID-19 work-from-home arrangement

Wang

Hsu

et al. 2022

Energy for Sustainable Development

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Photovoltaic rooftop's contribution to improve building-level energy resilience during COVID-19 work-from-home arrangement

Wang

Hsu

et al. 2022

Energy for Sustainable Development

View full text Add to dashboard Cite

“…Consequently, it becomes essential to explore the existing possibilities to cover the electricity needs only with totally renewable generation sources and with the assistance of the mentioned storage technologies. Different models are currently used to simulate microgrid and energy demand responses [30][31][32]. The Hybrid Optimization Model for Multiple Energy Resources (HOMER) has been used to carry out this comparative analysis of the different systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

et al. 2022

Self Cite

View full text Add to dashboard Cite

The decarbonization of the electric generation system is fundamental to reaching the desired scenario of zero greenhouse gas emissions. For this purpose, this study describes the combined utilization of renewable sources (PV and wind), which are mature and cost-effective renewable technologies. Storage technologies are also considered (pumping storage and mega-batteries) to manage the variability in the generation inherent to renewable sources. This work also analyzes the combined use of renewable energies with storage systems for a total electrification scenario of Grand Canary Island (Spain). After analyzing the natural site’s resource constraints and focusing on having a techno-economically feasible, zero-emission, and low-waste renewable generation mix, six scenarios for 2040 are considered combining demand response and business as usual. The most optimal solution is the scenario with the maximum demand response, consisting of 3700 MW of PV, around 700 MW of off-shore wind system, 607 MW of pump storage, and 2300 MW of EV batteries capacity. The initial investment would be EUR 8065 million, and the LCOE close to EUR 0.11/kWh, making the total NPC EUR 13,655 million. The payback is 12.4 years, and the internal rate of return is 6.39%.

show abstract

“…More specifically, rooftop photovoltaic systems are one of the most promising energy generation sources for prosumers in big cities, as more and more PV panels are installed 10 . Studies have shown that rooftop PVs can cover almost completely the domestic electricity demand for prosumers, indicating that self-sufficient cities are expected to be protagonists of the energy transition era 11 . However, efficient prosumption schemes are based on accurate solar production forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning strategies for solar power forecasting under data scarcity

Sarmas

Dimitropoulos

Marinakis

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Accurately forecasting solar plants production is critical for balancing supply and demand and for scheduling distribution networks operation in the context of inclusive smart cities and energy communities. However, the problem becomes more demanding, when there is insufficient amount of data to adequately train forecasting models, due to plants being recently installed or because of lack of smart-meters. Transfer learning (TL) offers the capability of transferring knowledge from the source domain to different target domains to resolve related problems. This study uses the stacked Long Short-Term Memory (LSTM) model with three TL strategies to provide accurate solar plant production forecasts. TL is exploited both for weight initialization of the LSTM model and for feature extraction, using different freezing approaches. The presented TL strategies are compared to the conventional non-TL model, as well as to the smart persistence model, at forecasting the hourly production of 6 solar plants. Results indicate that TL models significantly outperform the conventional one, achieving 12.6% accuracy improvement in terms of RMSE and 16.3% in terms of forecast skill index with 1 year of training data. The gap between the two approaches becomes even bigger when fewer training data are available (especially in the case of a 3-month training set), breaking new ground in power production forecasting of newly installed solar plants and rendering TL a reliable tool in the hands of self-producers towards the ultimate goal of energy balancing and demand response management from an early stage.

show abstract

Analysis of the potential for PV rooftop prosumer production: Technical, economic and environmental assessment for the city of Valencia (Spain)

Cited by 65 publications

References 30 publications

Photovoltaic rooftop's contribution to improve building-level energy resilience during COVID-19 work-from-home arrangement

Photovoltaic rooftop's contribution to improve building-level energy resilience during COVID-19 work-from-home arrangement

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

Transfer learning strategies for solar power forecasting under data scarcity

Contact Info

Product

Resources

About