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.
Concept
Energy Storage + Renewable
+ Conventional Engineering
- Integrating ENERGY STORAGE (both chemical and mechanical)
- Conventional power plants (gas/coal)
- Renewable generation (wind/solar)
Financial + Engineering
- Merges the Financial optimization and the Engineering - planning/designing and operations for integrating
- ENERGY STORAGE solutions with highest returns.
AI machine learning
algorithms and modeling
- Big-data simulated scenarios from historical weather
- Demand curves
- Financial markets power pricing
Renewable Energy
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.
Fossil Plants
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.
Foundational Software IP
MADA Analytics software is built on a unique foundation of optimization and process control
innovations, developed by key personnel in the company.
Method, system
and medium
for controlling
manufacture process
having multivariate
input parameters
System and method for complex process optimization and control
Method, system and medium for controlling manufacturing process using adaptive models based on empirical data
Model predictive control (MPC) system using DOE based model
Methods and apparatus for early fault detection and alert generation in a process