Description
To exploit the difference in electricity prices and maximize system profit, a battery storage system could be installed to store excess energy when electricity prices are low and discharge it when prices are high. In most cases, this can be done by setting up a smart charge and discharge schedule. Or with the BESS CLOUD AI cloud platform, which automatically arbitrates market prices by minute and anticipates seasonal changes in electricity prices.
To compensate for the local limitation of the transformer, it is important to ensure that the charging and discharging of the battery does not exceed the capacity of the transformer. This can be achieved by monitoring the transformer load and adjusting the battery charge and discharge schedule accordingly.
Here are some steps to follow to implement this system:
Install a battery storage system that can store excess energy during off-peak hours when electricity prices are low.
Set up a smart charge and discharge schedule that takes into account the time of day, day of week and seasonal changes in electricity prices. This schedule should prioritize charging the battery during off-peak hours and discharging it during peak hours when prices are high.
Monitor the transformer load to make sure the battery charge and discharge does not exceed its capacity. If the transformer load is approaching its limit, adjust the battery charge and discharge schedule to reduce the transformer load.
Use software and data analysis tools to optimize charging and discharging schedules based on historical and real-time electricity prices, as well as other factors such as weather conditions and energy demand.
By implementing this system, it is possible to reduce energy costs, increase system efficiency and minimize the impact on the local transformer.
With the option to integrate and manage energy resources. One of the advanced ideas in this context is the development of an EMS (Energy Management System) that not only predicts the stock exchange with automatic balancing, but also includes a hardware monitoring system, providing autonomy and efficient management.
1. Forecasting the energy exchange with automatic balancing and day-ahead forecast and analysis:
Data Analysis: To build a successful EMS system, it will be necessary to use machine self-learning techniques and predictive algorithms. The study and analysis of data from the electricity trading exchanges will provide you with the basis for creating a model to forecast market movements.
Artificial intelligence for balancing: the development of an AI module will allow you to automate the decision-making and analysis processes for charging and discharging in multi-profile variants. The module should be able to analyse data from the electricity markets and take actions such as buying, selling and portfolio balancing to maximise profit and reduce risk.
2. EMS Trading Platform without human intervention:
Automated Trade Moves: Develop a system that uses predefined algorithms and rules to automatically execute trade orders. This module must be able to react quickly to changes in market conditions and execute orders in real time.
Using API for trading: integrate the EMS system with the API of the power exchanges, which will allow automatic execution of trade orders. This approach will provide you with fast and efficient access to market information and the ability to react quickly day ahead.
3. Self-Service Hardware Monitoring System:
Sensors and IoT devices: integrate various sensors and IoT devices that will collect data on energy efficiency, financial performance and other parameters. This can include sensors for power consumption, temperature, humidity and other important metrics with forecast for climate patterns tied to electricity consumption.
Data management: develop a data processing and analysis system that will automatically process the information from the hardware system. Use technologies such as databases, cloud solutions and analytics tools to extract value from the data collected.
4. Virtual PV Synchronization:
Integrate virtual photovoltaic (PV) systems: develop functionality to integrate virtual PV systems into EMS. This will allow optimisation of energy production taking into account financial and energy targets.
Production resource management: implement algorithms that will automatically manage the use of virtual PV resources. This module must be capable of regulating power production in real time to maximize efficiency.
