e4c (energy4cast)

Wind and Solar Park owners do not know their actual daily energy production potential -they only know their nominal capacity; 

Thus, Park owners cannot maximize revenue nor optimize operations;

As energy cannot be stored efficiently yet, and Park owners do not know their actual daily maximum energy production potential in advance, energy is lost by not being traded;

Energy system operators require and/or need prior firm commitment in order to meet energy demand;

Feed-in-premium schemes require a prior firm commitment of the Park’s energy output (e.g., 48 hours in advance) in order to manage energy demand efficiently.

Proprietary Energy Yield Tool for measuring renewable energy management success: output and revenue versus potential output and revenue of renewable energy parks;

Optimizes energy management, mitigates energy waste, and maximizes a Wind or Solar Park’s revenue;

Accurately forecasts, location-specific wind or solar energy over a site
in 48-hour time intervals based on synoptic meteorological data;

Utilizes post processing tools based on advanced statistical methods
–e.g., bias correction methods, Kalman filter application etc.

Intersects two software tools:

1. An innovative Atmospheric Prediction Model (APM) to analyze and predict meteorological data on a 250-1000m grid; and
2. An Energy Prediction Model (EPM) of a Park’s wind or solar power up to 48 hours in advance

APM = meteorological macroscopic methods + meteorological synoptic data + local topological + aerodynamic data

• Very high-resolution atmospheric model
• Reduces wind speed RMSE by ~20% and BIAS by ~40%
• Resolves main features of the turbulence usually prevailing in lower part of the atmospheric boundary layer (ABL) and affecting the quality of wind flow around the rotors
• Supports ensemble forecasting providing probabilistic wind forecasts instead of the trivial deterministic ones

EPM:

• Based on physical models and not empirical ones 
• Solves the Navier Stokes equations (the complete equations that govern fluid motion) on a fine grid, including refinement techniques, accommodating a vast number of nodes over areas of interest
• Different from competitors who use either semi-empirical models (e.g., WASP), or physical models based only on mass equations including boundary layer corrections

Demo version of our basic APM operational product

Our publications validating globally our Atmospheric Prediction Model as the basis of our Energy Yield Tool; indicatively:

https://www.sciencedirect.com/...  Louka, P., G. Galanis, N. Siebert, G. Kariniotakis, P. Katsafados, G. Kallos, and I. Pytharoulis, 2008: “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering”. Wind Engineering and Industrial Aerodynamics, 96 (12), pp. 2348– 2362

Our solution impacts every citizen / energy consumer.

We expect that our solution will have a particularly significant impact on underserved communities and MSMEs including women- and minority-businesses. 

Among other: The former will benefit from cheaper and more green energy available. The latter will benefit from cost optimization and energy usage rationalization.

The health and climate benefits of wind / solar are larger than its grid-system value. We will preserve and enhance (a) Social Capital by helping farmers and farmer communities to sustain their operation and become more resilient with cost efficiencies from optimal wind / solar power utilization; (b) Manufactured Capital by ensuring safety and compliance along the value chain; and (c) Natural Capital by mitigating terrestrial biodiversity loss and supporting living landscapes.

We are optimizing:

• energy yield from a Wind or Solar Park -thus making more green energy available and lowering RES energy consumption cost;
• the subsequent capability of the Park owner to manage the Park's energy output efficiently -by accurately forecasting this output;
• energy savings -by controlling RES energy waste;
• energy cost for the citizens -by eliminating RES energy waste;
• utility’s energy management -by informing / committing to the utility in advance of / with accurate and optimal energy intake potential;
• trade volume; decisions on how and where to trade the energy production, and at which price;
• grid management.

The executive team behind our solution is comprised of individuals who are driven by DEI principles, recognize their responsibility of giving back to the community, and are interested in making an impact to serve future generations.

The team's combined professional knowledge serves the team's societal interests by providing a thorough understanding of our solution's feasibility to serve those interests and address the respective societal needs.

We believe that our solution's potential to impact society is very broad. As such, we believe that our multidisciplinary team with diverse community involvement, serves this broad purpose well.

Andreas is a scientist and has taught over 4,000 children and students to date about renewable energy and entrepreneurship.

Sandy has lead STEM activities and other training programs, women entrepreneurship, organizational change, gender and social development, including as a senior executive in Fortune 500 companies.

Helena has been involved with education in parallel to her engineering and environmental endeavors.

Manos is an ICT and financial services expert with a parallel profound interest in the performing arts, which serves his pro bono work.

Petros is a climate and environmental expert; and an academic leading youth scientific endeavors.

Tania has been involved with impact finance throughout her investment banking career; with philanthropy; and with women entrepreneurship.

Theo, the Team Lead, is a climate expert adviser with the EBRD; associates with various children causes incl. the Justice Initiative; is involved with biodiversity and sustainability in food & agriculture incl. through own investments (viti- and viniculture; vertically integrated clean label juice production; etc.); launched the first Social related campaign in Greece in 2008 about women leadership and importance in agriculture; is part of Zell and the Zell Lurie Institute, and of two institutions teaching and practicing philosophy; has advised the Head of Business Ethics of the Centre for Corporate Responsibility and Sustainability at the University of Zurich; and other.

Also, the team are partners with Theo's organization, Agoge Ventures. Agoge works with international donors such as the World Bank and EBRD to bridge the finance gap, to promote women and youth entrepreneurship, and to advance STEAM education (incl. for girls) in the donors' territories. The team is involved throughout.

We have invested in and built our APM and EPM software (and need to complete the functionality facing the customer).

We have tested our solution in two pilot projects in Greece (and seek our pilot in the U.S.).

Our publications serve as a scientific validation of our solution.

We have significant raw data.

We have developed our solution with human (scientists) and other resources in Greece. With the U.S. being our home market, we hope for Solve to bridge our solution's transition to the U.S.

Within this framework, we hope for Solve to assist us: 

• with strategic decisions such as whether we should maintain our scientific base (R&D) in Greece, taking advantage of synergies in costs;
• as a launch pad for the U.S. markets (global leaders in RES);
• with synergies concerning other technologies (e.g., AI);
• with an ecosystem to exchange ideas, advance research, connect to networks, promote and enhance our social impact;
• with tools, guidelines, input, and applications to advance our business planning and operational structures;
• with funding potential.

Advanced Algorithmic Foundation

• NWP, CFD, EPM(Energy Prediction Model), APM (Atmospheric Prediction Model)
• E4C stands out for its precise and adaptable forecasting, powered by a blend of advanced modeling techniques, tailoring to short-term wind energy predictions.

Customization and Data Utilization

• Bias correction, Kalman filter, advanced statistical methods, physical models
• E4C's approach is highly customizable, integrating diverse data and sophisticated models for nuanced forecasts

We are able to predict on a high resolution of 250-1000m grid with an accuracy of up to 40% greater than the market. This accurate down-scaling leads to maximizing the accurate and timely prediction of the energy output. We are innovating by having developed the most accurate atmospheric prediction tool, combining, meteorological macroscopic methods and meteorological synoptic data and local topological and aerodynamic data. Energy4cast utilizes a number of post processing tools based on advanced statistical methods such as bias correction methods, Kalman filter application etc.   

Our EPM is based on physical models and not empirical ones. We solve the Navier Stokes equations (the complete equations that govern the fluid motion) on a fine grid, including refinement techniques, accommodating a vast number of nodes over areas of interest. Most of our competitors use either semi-empirical models (e.g., WASP), or physical models based only on the mass equations including boundary layer corrections.   

Our APM is implementing a very high-resolution atmospheric model able to reduce the wind speed RMSE by ~20% and the BIAS by ~40% (see also above publication under “Validation”).  It is also able to resolve the main features of the turbulence usually prevailing in the lower part of the atmospheric boundary layer (ABL) and affect the quality of the wind flow around the rotors. It supports the ensemble forecasting providing probabilistic wind forecasts instead of the trivial deterministic ones.

Our Solution is using physical models and providing probabilistic wind forecasts. We are solving the complete Navier Stokes and Euler equations. Our Solution is thus very accurate in forecasting daily energy output.

Our publications (selected):

https://www.sciencedirect.com/science/article/abs/pii/S0167610508001074

“Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering”

“A fully coupled Atmosphere–Ocean Wave modeling system (WEW) for the Mediterranean Sea: interactions and sensitivity to the resolved scales and mechanisms”. Geoscientific Model Development, 9(1), 161-173, doi: 10.5194/gmd-9-161-2016

“Implementation of a two-way coupled atmosphere-ocean wave modeling system for assessing air-sea interaction over the Mediterranean Sea”. Atmospheric Research, 208, 201-217, doi: 10.1016/j.atmosres.2017.08.019

“Assessing the implicit rain impact on sea state during hurricane Sandy (2012)”. Geophysical Research Letters, 45, 12015-12022, doi: 10.1029/2018GL078673

“Investigating the impact of atmosphere–wave–ocean interactions on a Mediterranean tropical-like cyclone”. Ocean Modelling, 153, 101675, doi: 10.1016/j.ocemod.2020.101675

“One-year assessment of the two-way coupled atmosphere-ocean wave modeling system CHAOS over the Mediterranean and Black Seas”. Mediterranean Marine Science, 21(2), 372-385, doi: 10.12681/mms.21344-2020

“Investigating sea-state effects on flash flood hydrograph and inundation forecasting”. Hydrological Processes, 35(4), e14151, doi: 10.1002/hyp.14151-2021

We shall contribute to, among other:

1. making (green) energy available to and cheaper for larger population
2. providing cheaper (green) energy to communities and small businesses, thus enabling sustainability, competitive advantage, cost efficiencies, operations optimization, and growth
3. climate risks mitigation
4. making cheaper basic goods available to larger population

Our base indicator:

The positive differential between the energy traded currently by a Park and the (higher) actual energy available to be traded by the same Park after using our solution -i.e., energy savings by controlling energy waste.

On that premise, we are working on our Social and Environmental, and building indicators relating to businesses' operating cost, citizens' energy consumption cost, communities' offerings / support to their members, among others.

We also aim at creating a repository, which we shall use directly and indirectly to improve life in underserved communities. We will be pleased to elaborate in a next phase of the Solve process. We are developing respective models and potential applications in food, health, and education, among others. 

Our indicative contribution to the UNSDG:

2.3 (target); 2.3.2 (indicator)

2.4; 2.4.1

7.1; 7.1.2 - 7.2; 7.2.1 - 7.3; 7.3.1 - 7.b; 7.b.1

8.2; 8.2.1

9.2; 9.2.1 - 9.4; 9.4.1

12.2; 12.2.1 / 2 - 12.a; 12.a.1

13.2; 13.2.2

Our Energy Yield Tool consists of two software, namely a) a numerical weather prediction model -i.e. the Atmospheric Prediction Model (APM) and b) a numerical wind power micro-scale prediction model -i.e. the Energy Prediction Model (EPM). Specifically, APM, which is based on our atmospheric model, provides all the key variables of the Atmospheric Boundary Layer in the vicinity of the (Wind) Park. Then, EPM, based on a Large Eddy Scale turbulence model, down-scales the mesoscale atmospheric circulation to the turbulent flow inside the (Wind) Park, and estimates the energy production of each Wind Turbine. The entire sequence of the simulations are executed on a daily basis and the end user (Park owner, energy aggregator etc.) is able to implement the respective energy forecast as a decision support system well prior to committing to an energy contract or auction. 

Please see some of our publications:

Papadopoulos, A., P. Katsafados, G. Kallos, and S. Nickovic, 2002: “The weather forecasting system for POSEIDON-An overview”. Global Atmosphere and Ocean System, 8, 219-237.  

Galanis, G., P. Louka, P. Katsafados, I. Pytharoulis, and G. Kallos, 2005: “Applications of Kalman filters based on non-linear functions to numerical weather predictions”. Annales Geophysicae, 24, 2451–2460.  

Louka, P., G. Galanis, N. Siebert, G. Kariniotakis, P. Katsafados, G. Kallos, and I. Pytharoulis, 2008: “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering”. Wind Engineering and Industrial Aerodynamics, 96 (12), pp. 2348– 2362.  

Karagiorgos G., P. Katsafados, A. Kontarinis, N. M. Missirlis, and F. Tzaferis, 2008: “Load Balancing for the Numerical Solution of the Navier-Stokes Equations”. Lecture Notes in Computer Science (LNCS), 4699, Springer, doi:10.1007/978-3-540-75755-9, pp. 764-773.  

Papadopoulos A. and P. Katsafados, 2009: “Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean”. Natural Hazards and Earth System Science, 9, 4, pp. 1299-1306.  

Katsafados P., S. Kalogirou, A. Papadopoulos, and G. Korres, 2012: “Mapping long-term atmospheric variables over Greece”, Journal of Maps 8 (2), pp. 181-184.  

Katsafados, P., A. Papadopoulos, G. Korres, and G. Varlas, 2016: "A fully coupled atmosphere–ocean wave modeling system for the Mediterranean Sea: interactions and sensitivity to the resolved scales and mechanisms". Geoscientific Model Development, 9, 161-173, doi:10.5194/gmd-9-161-2016. 

Takvor Soukissian, Anastasios Papadopoulos, Panagiotis Skrimizeas, Flora Karathanasi, Panagiotis Axaopoulos, Evripides Avgoustoglou, Hara Kyriakidou, Christos Tsalis, Antigoni Voudouri, Flora Gofa, Petros Katsafados, 2017: “Assessment of offshore wind power potential in the Aegean and Ionian Seas based on high-resolution hindcast model results”. AIMS Energy, 5(2), 268-289, doi: 10.3934/energy.2017.2.268.

The executive team below is part-time.

Petros is leading our partnership with the Harokopeion University of Athens (Greece), managing a team of three researchers related to our APM.

Andreas is managing one researcher related to our EPM.

Helena is managing VEA Foundation Inc. (USA), our alliance with respect to climate risks mitigation efforts.

Theo has managed the University of Michigan - Ross School of Business team (Master of Management Consulting Studio within the U-M MAP), yielding a study on market fit and U.S. markets penetration.

It has taken us 10 man-years to arrive to the current accuracy levels and stage of solution readiness -TRL 7. This includes the development of both our models -APM and EPM.

Our executive team:

Theo Theoharis, Founder and acting CEO; MBA, The University of Michigan Stephen M. Ross School of Business; Over 500 public and private SME and start-up international transactions with a total value of over $7 billion

Andreas Koras, Founder and Chief Energy Technologist / CPO; PhD in Physics and Aerodynamics, the University of Athens; Research in modeling: subsonic, compressible, turbulent, one- or two-phase fluid flows, subsonic, incompressible, fluid flows around lifting or non-lifting bodies

Helena Thornton, COO; Certified in Energy and Environmental Management (CEEM); BS I&SE; U of Michigan-Dearborn; MS I&SE; U of Michigan-Rackham; 25+ years of experience in automotive manufacturing. manager in Cost-Technology Optimization - drove systematic part standardization saving over $2.5M/year

Konstantina (Tania) Galani, CIO; BA and MA in Philosophy, Politics and Economics from Oxford University; 20+ years of experience in investment banking and client relationship management; Corporate Finance (Lazard), Financial Sponsors, Marketing, Investor Relations, Fundraising for Alternative Investments & companies ($1.2 billion in capital raised)

Petros Katsafados, Chief Scientist.; PhD in Mathematics and Atmospheric Physics and Dynamics, the University of Athens; 40+ articles in scientific journals; 85+ conference papers; 896 citations.; Research in atmospheric dynamics, regional & mesoscale modelling, and data assimilation

Manos Margaritis, Chief Technology Officer; Stanford Graduate School of Business and London Business School; MSc in Telecommunications Engineering, the Imperial College; Electrical Engineering Diploma, NTU of Athens.; 25+ years of experience as CIO, CTO, and Deputy CEO in the ICT sector

Sandra L. Dietrich, CQO / CAO; PhD in Interdisciplinary Technology from Eastern Michigan University; MSc in Applied Statistics; 30+ years of executive product design, engineering, manufacturing experience with Ford, GM, Terumo Medical

Theo's organization, Agoge Ventures, works with international donors such as the World Bank and EBRD to bridge the finance gap, to promote women and youth entrepreneurship, and to advance STEAM education (incl. for girls) in the donors' territories. The above executive team is involved throughout.

Theo is associated with the Justice Initiative (.eu).

We have a DEI policy in place, as well as related planning for our Social and Human Capitals.

We envision to empower small communities toward their sustainability, and WMSMEs and minority MSMEs to compete. Indicatively, 95% of 570 million farms worldwide, which are less than 5 hectares in size, could benefit directly or indirectly from our solution.

We shall impact every citizen/energy consumer by mitigating energy waste/loss through offering our Energy Yield Tool B2B to (a) onshore and offshore park operators incl. direct electricity transactions to (large) electricity consumers, (b) full-service providers (investor-owned utilities; public entities such as municipalities, state power agencies, and municipal marketing authorities; federal entities; cooperatives), (c) other providers incl. electricity marketers and electricity trading on wholesale marketers or through bilateral contracts, and (d) investors and financiers in the field. Independent power producers own the majority of wind assets -73% of the new wind capacity installed in the United States in 2020, with the remaining assets (27%) owned by investor-owned utilities. The percentage shares of electricity sales by type of provider in 2019 in the U.S. were: investor-owned utilities -57%; public and federal entities -16%; cooperatives -12%; other providers -16%.

We aim at optimizing the renewable energy value chain by assisting the wind and solar park owners, the FERC, ISOs, and RTOs to utilize the maximum energy yield available every day.  Such optimum utilization shall result in, among other, cost (and grid) efficiencies for the benefit of the citizens and MSMEs -direct as energy consumers; and indirect as consumers of product/services (produced on cheaper and more efficient energy).

To date we have developed our solution (a) with own investment, and (b) through a scientific research partnership with the Harokopeion University of Athens (HUA) in Greece.

We will monetize our services by offering our Tool as a Service.

Service Fees to be paid by (wind and solar) Park owners for receiving daily energy output 48 hours in advance; and

Management Fees to be paid by Park owners for managing all or part of:

• trade their KWH output in advance, and thus
• project accurately their income streams
• switch buyers or secure better price or secure the highest possible price
• decide how and where to trade their energy production
• optimize their trade volume by controlling energy waste or eliminating this altogether
• gain additional or supplemental income by trading surplus energy output for Green Certificates

Service Fees to be paid by (a) Fire Departments for receiving wind flow over a burning wood 48 hours in advance; (b) Energy Traders for receiving probable excess energy available for trading 48 hours in advance; (c) System Operators, who currently know the energy demand but not the actual RES capacity; (d) Municipalities; (e) industries such as power plants, oil & gas, data centers & cloud services providers, smart homes and commercial buildings.   

Moreover, we expect to incur revenue from the Green Certificates, our Insurance Product (in the planning phases), consulting fees and big data analytics. Eventually, our Tool will be also used for the selection of the most appropriate locations for the installation and effective operation of Parks.