MADA Analytics’ MEPS™ software operates as follows: MEPS™ receives predicted demand and energy price for a given period of time, together with live information and predictions of the total energy generation available from the renewable energy sources. These parameters cannot be controlled by the system but will influence it. MEPS™ then calculates the total energy needed to meet the load of the grid. During periods in which the energy received from renewable sources is in excess of that needed to meet load, MEPS™ will send an order to use the excess renewable energy to charge energy storage. During periods in which the load exceeds available renewable energy, MEPS™ will order the storage unit to discharge and generate electricity to the grid.
In cases that the energy generated from both the renewable energy and the discharge of the storage is still not sufficient to satisfy the grid’s load, the MEPS™ will order the fossil fuel sources to generate the additional energy required to fill in the remaining gap. All orders generated by the MEPS™ will take into consideration the constraints of the various generators and other system components. MEPS™ incorporates a broad range of domain expertise to allow the optimization of hybrid renewable energy project design and operation, including:
MEPS™ analyzes, integrates, and utilizes energy storage solutions as the central component to smooth out the energy supplied from intermittent renewable energy sources, which are inherently a “noisy” unstable source of energy. Shaping the renewable energy occurs in periods of both excess supply and supply deficiency, in both short and prolonged durations, and in high and low frequencies. MEPS™ contains the parameters required to accurately model different energy storage systems and combination of different storage technologies, allowing the user to compare the ability of different energy storage configurations to meet a project’s needs. Constraints such as size, life cycle, ramp rates, etc., are all taken into consideration as are their financial implications for the project. Ultimately MEPS™ will guide the storage system operational regimes, ordering it to charge or discharge at specific capacities for specific intervals of time.
Energy Storage + Renewable
+ Conventional Engineering
Financial + Engineering
AI machine learning
algorithms and modeling
MEPS™ predicts the wind and solar power generated at a specific location using industry standard engineering models. For project assessment and planning, the software determines optimal sizing, renewable type, and placement of renewable fields, as well as managing the generated energy, directing it either to the grid or to storage. As a result, renewable energy is delivered to the grid in a reliable and controlled manner. Capital and operating costs are kept up to date based on industry data.
While it may at first seem counterintuitive to consider gas turbines or other fossil-fueled plans in the context of renewable energy projects, at times they can play a valuable role. MEPS™ can integrate and utilize a fossil power plant to serve as a backup power facility, thus guaranteeing a reliable supply of energy. MEPS™ takes into consideration all of the operating parameters, requirements, and limitations of multiple types of fossil power plants as possible backup.
MEPS™ optimizes system selection, sizing, and operations
of the different facility components to achieve financial
performance targets and desired emission reductions. Different
projects may have different priorities, resulting in a different
system configurations and operating regimes. For example,
some users may wish to maximize IRR, while others want to
maximize annual energy output. In all cases, MEPS™ will provide
reliable, cost-effective, load-following power, while radically
reducing greenhouse gas emissions. The algorithms underlying
the MEPS™ optimization, originally developed and patented
by MADA Analytics Co-Founder Yossi Fisher for optimization
and process control in the semiconductor industry, have been
refined over 30 years.
Market power pricing:
Data feeds of real-time and/or predictions of power market pricing and available supply are fed into the MEPS™, providing an additional facet of the analysis and optimization. When prices are low, MEPS™ will direct the facility to buy power to supplement low renewable generation or to store energy for later sale; when market prices are high, the analytics will suggest selling overcapacity from the renewable generation or discharge storage. These decisions are made using sophisticated model predictive analytics to generate high economic returns by both capitalizing on pricing opportunities and reducing other power equipment usage when possible. Provision of ancillary services like regulation and spinning reserves are considered in the project economics.