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Energy Optimization Modules
Capable of handling the expansion of renewable energy and changes in the energy mix.
Large-scale problems can also be solved efficiently.
Not only can it predict demand, but it can also optimize while predicting dynamically fluctuating uncertainties.
The Need for Integrated Optimization
With the expansion of renewable energy and the changing energy mix, it is inefficient to rely solely on experience and intuition using traditional fixed systems centered on fossil fuels, as there are many areas where it is difficult to cope with the changes, resulting in significant losses.
Furthermore, instead of performing demand forecasting with machine learning and then optimizing decision-making, it is possible to calculate them simultaneously with a fusion model of forecasting and optimization using MOAI technology. This approach is already being applied in some overseas locations.
Applicable Problems
１．Optimization of Unit Commitment Problem with Security Constraints
Based on demand forecasts that consider dynamically fluctuating uncertainties, this plan optimizes the operation schedule of power generation units and secures the required power generation with minimal operating costs and minimal environmental impact. In recent years, renewable energy sources have been added to the existing fossil fuels, and the problem has become larger in scale, making efficient calculation impossible without advanced optimization technologies such as MOAI.
２．Power Market Trading Optimization
In power trading on the market, it is possible to develop trading strategies such as pricing and buying/selling timing based on demand forecasts. Maximize revenue by selling at the optimal price while adhering to various regulations, resources, and constraints related to maintaining supply-demand balance.
３．Optimization of Optimal Power Flow (OPF)
Real-time performance is required for optimal power flow (OPF) optimization. It is extremely difficult to perform optimization with manual adjustments alone, leading to significant losses. Overseas ISOs are achieving real-time quasi-optimization using a method that integrates deep learning and optimization. MOAI Lab has developed SOTA algorithms for both AC and DC power flow and is realizing speed improvements through deep learning.
Optimization Algorithm Performance
Regarding optimization algorithms, we conduct experimental analysis on as many benchmark problem instances as possible, and have confirmed that they perform as well as or better than state-of-the-art (SOTA) algorithms. In addition, we have achieved speed improvements of several to hundreds of times compared to SOTA algorithms by using our unique technologies such as the fusion of machine learning and optimization algorithms (MOAI) and automatic selection from multiple optimization algorithms.
How to Implement
- A Proof of Concept (PoC) using the MOAI platform is possible before implementation, allowing for quick validation of effectiveness.
- Customization is only performed when necessary.
- The service is provided as a cloud service (SaaS), but API provision and on-premise usage are also supported. Please contact us for details.