Challenges
We are seeking a digital solution leveraging artificial intelligence (AI) to identify the energy needs of clients (B2B and/or B2C) and analyze their energy consumption profiles (electricity, heat, gas, renewables).
The solution should generate energy optimization scenarios (e.g., energy storage, PV systems, heat pumps, PPA contracts) and support decision-making for energy investments and services within the Energy as a Service (EaaS) model. Key functionalities should include: technical, economic, and environmental analysis of proposed solutions; estimation of costs, ROI, emission reductions, and energy efficiency; application of AI algorithms to forecast consumption, costs, and profitability.
We expect a solution that supports dynamic and personalized energy advisory services tailored to individual clients.
We are looking for an advanced decision support system for energy flexibility services (primarily DSR). The solution should enable real-time verification of power portfolio readiness, assessment of capacity delivery capabilities, risk management, and data-driven decision-making based on subcontractor and operational profiles. The system should detect anomalies, forecast subcontractor performance, and support automated decision processes using AI.
We are looking for a solution that utilizes smart meter data assigned to medium-to-low voltage (MV/LV) transformer stations to assess risks related to power quality (e.g. voltage violations) and potential disconnections of photovoltaic (PV) installations. The use of AI is expected to analyze correlations between measurement data and weather conditions, taking into account seasonality.
The goal is to prioritize MV/LV stations that require intervention and to improve the management of low-voltage grid operations, without the need for detailed knowledge of the network topology.
We are seeking an analytical system that enables the integration of market data, price forecasts, and technical parameters of energy storage facilities to optimize charging and discharging strategies. The solution should support planning for the participation of storage units in the capacity market and balancing services, as well as enable automated operational decision-making based on historical and forecasted data using AI/ML technologies.
The system should allow integration with SCADA systems and have the ability to respond to signals from the LFC node. It should analyze technical constraints of storage units (such as capacity, operating cycles, efficiency) and support their optimal utilization while extending their lifespan. The ability to perform energy arbitrage and shift energy consumption over time to enhance the system's economic efficiency is desirable.
The solution is expected to assist operators in delivering system services and improve the efficiency of energy storage management in rapidly changing market conditions.
We are looking for IT solutions in the energy sector that enable the delivery of new services aimed at optimizing energy costs based on dynamic tariffs. ORLEN is particularly interested in solutions for prosumers that facilitate peer-to-peer trading of electricity between customers.
Software for conducting an energy audit aimed at selecting the optimal parameters for a battery energy storage system. Input data should include: consumption/generation profiles, energy consumption/production (15-minute intervals from the last 12 months), and - in the case of consumers - operational characteristics of the company, as well as installed and planned generation sources. Additional selection criteria should reflect user objectives such as arbitrage, RES optimization, peak shaving, capacity charge reduction, emergency power supply, and balancing market participation. Optional: financial analysis of the selected solution.
We are looking for a solution that will support revenue growth from electricity generation in small hydropower plants operating in a cascade configuration. The expected tool should optimize the timing of electricity production based on real-time flow and market data. The system should assist in operational decision-making and either integrate with existing infrastructure or function as an independent platform.
We are looking for a solution that enables effective management of an electric vehicle fleet along with the selection and operation of charging infrastructure. The system should support optimal vehicle allocation, usage planning, and charger placement, taking into account the geographic distribution of company facilities and the diverse operational tasks performed by employees. The solution should be capable of integrating with existing infrastructure or provide proprietary tools.
