Algorithms for Smart Battery Management
Best for
Solar farms
Wind power plants
Hybrid energy systems
Types of algorithms
In industry, business, and daily life, three main battery management algorithms dominate:
AI-Based Optimization
Algorithm
This is the most advanced battery management algorithm, utilizing artificial intelligence to predict energy consumption needs and energy production (e.g., from a solar plant). The goal is to minimize energy costs, grid electricity usage, and electricity expenses.
The AI-Based Optimization algorithm selects the most efficient battery operation scenario. For example, it charges the battery only as much as needed for a specific period, considering the user’s predicted needs and expected energy generation. The battery is discharged during peak price or demand hours, avoiding the most expensive grid electricity.
The AI-Based Optimization algorithm combines the advantages of both Self-Consumption and Day Ahead algorithms and optimizes energy flows.
With this algorithm, the battery investment pays off in 6 years.
Requires specialized software.
Self-Consumption
Algorithm
During the day, when a solar power plant generates energy and the user’s demand is low, excess energy is directed into the battery. When the user’s energy demand exceeds the energy generated by the solar plant (e.g., in the evening or at night), the stored energy in the battery is discharged and used instead of drawing from the grid.
The algorithm automatically monitors surplus and shortage conditions, charging or discharging the battery accordingly.
- Ensures efficient use of solar-generated energy.
- Useful when there is no electricity “storage” service, i.e., when zero tariffs apply for excess energy fed into the grid.
- Due to the seasonal operation of the solar power plant, the battery is not utilized optimally. In late autumn, winter, and early spring, it is almost unused due to the lack of excess energy.
- With this algorithm, the investment in a battery pays off in 30–50 years.
Day Ahead
Algorithm
This algorithm manages battery charging and discharging based on the predicted electricity prices for the following day.
It aims to minimize energy costs by charging the battery when electricity prices are at their lowest and discharging it when prices are at their highest.
- Helps optimize energy usage and saves more compared to the “Self-Consumption” algorithm.
- Not fully customized for a specific user’s energy needs, making it inefficient in cases of fluctuating electricity consumption.
- If the user also utilizes solar energy, the algorithm may not account for its contribution.
- Creates complete battery discharge cycles, which accelerate battery wear.
- When electricity price differences are minimal, battery cycles may be used in a way that costs more than the savings achieved.
With this algorithm, the battery investment pays off in 18–19 years.
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The Cost Minimization
This scenario maximizes battery usage, electricity price fluctuations, and consumption forecasts to achieve the highest financial benefit.
Key benefits
Users consume the cheapest electricity, reducing their bills.
Opportunity to sell energy back to the grid at a higher price.
The algorithm predicts both energy consumption and generation to achieve the best results.
Minimizes battery wear by utilizing partial discharge cycles.
Best suited for
Situations where electricity prices fluctuate significantly throughout the day (e.g., on the Nord Pool exchange).
Opportunity to sell energy back to the grid at a higher price
Those looking to reduce costs through smart energy use and resale.
This scenario is relevant for households that can take advantage of electricity exchange prices and want to reduce energy costs. It is also suitable for businesses that consume a lot of energy and can optimize their usage based on price changes. Additionally, it benefits companies looking to maximize energy arbitrage profits and participate in balancing services.
Example
The Self-Consumption
This scenario maximizes the use of self-generated electricity and reduces dependency on the grid.
Key benefits
During the day, excess solar energy is stored in the battery.
At night or during low solar generation periods, the battery supplies energy.
Reduces or eliminates grid electricity consumption when battery capacity and generation are sufficient.
Uses battery energy when grid prices are highest and demand is high.
Best suited for
Countries where grid energy export is restricted or compensated at a lower rate than grid electricity costs.
Users wanting to reduce network usage fees or energy storage charges.
Regions with high solar energy generation potential (e.g., Southern Europe).
This scenario is a universal solution for households and small commercial users to maximize renewable energy usage.
Example
The Peak Shaving
This scenario reduces peak energy consumption, preventing demand from exceeding a specific threshold or avoiding high electricity costs during peak times.
Key benefits
The battery is charged when demand is low or electricity is cheap.
Stored energy is used during peak demand, preventing power overuse or costly peak tariffs.
Avoids penalties for exceeding power demand limits.
Best suited for
Businesses paying high charges for peak power demand.
Situations where electricity prices spike during peak hours.
Consumers with limited infrastructure capacity, preventing an increase in grid connection power.
This scenario is particularly useful for large energy consumers that pay for reserved power capacity, helping to lower monthly fees. It is also beneficial for households with high-power appliances (e.g., heat pumps, electric ovens), which cause spikes in consumption.
Example
The Self-Sufficient
This scenario ensures the user generates and consumes only as much energy as needed, rather than maximizing generation capacity.
Key benefits
Energy is generated and stored only in necessary amounts to meet the user’s needs until the next production period.
Prevents overloading batteries with excess energy.
Reduces reliance on the grid.
Best suited for
High electricity prices but low returns for grid energy exports.
Regions where grid energy export is restricted.
Users who want to extend battery lifespan.
This scenario is ideal for small businesses or farmers looking to meet their energy needs independently.
Example
The Curtailment
This scenario manages situations where the grid cannot accept or limits excess energy exports.
Key benefits
Reduces grid load and avoids power disruptions from overproduction.
Helps comply with local grid regulations.
Prevents penalties for exceeding grid capacity limits.
Best suited for
Grid operators imposing strict export limits.
Situations where grid infrastructure cannot accept additional energy.
Users who cannot invest in larger energy storage solutions.
This scenario is particularly relevant for large solar or wind power plants, areas with grid constraints, and energy suppliers needing to comply with forecasted production limits.