Self-Consumption Optimization & Peak Load Shaving

Battery storage systems are considered a key technology in the energy transition because they make fluctuating renewable energies like solar and wind power more flexibly usable. Both private households and businesses can benefit from Behind-the-Meter (BTM) battery storage. BTM storage is used to 'reduce' electricity consumption 'behind the meter', save energy costs, and reduce grid fees.

There are three main use cases that BTM battery storage can specialize in:

(1) Self-consumption optimization (use as much of your self-generated solar power as possible),

(2) Peak shaving (cutting expensive power peaks in electricity consumption), and

(3) a combination of both strategies.

The following sections provide a practical and understandable explanation of these use cases, including their implementation in simulations and realization in real practice.

1. Self-consumption optimization – utilize more solar power yourself

The goal of self-consumption optimization is to maximize the share of self-produced power from a PV system. In practice, this means storing the surplus solar power produced at noon in the battery to use it in the evening or at night when needed. Without storage, excess PV power often has to be fed into the grid (often at a lower remuneration), while during less sunny hours, power is expensive to purchase from the grid. A battery storage system can bridge this gap.

How is this technically implemented? A simple controlling algorithm charges the battery whenever PV generation > consumption (surplus power), and discharges it when consumption > PV generation (deficit), as long as the battery state of charge (SoC) allows it. This minimizes grid consumption. Mathematically, the self-consumption strategy can be formulated as cost minimization: minimizing electricity procurement costs over a period by keeping the power drawn from the grid low and avoiding feed-ins (for which there is only a low remuneration). Simply put, the goal is: “Consume as much of your own solar power as possible, buy as little grid power as necessary”. In a simulation, this could be achieved with an optimization model that decides for each time interval whether to charge, discharge, or leave the battery idle—while observing battery capacity and performance limits.

Implementation in energy management: Many Energy Management Systems (EMS) today use forecasts for PV generation and consumption to proactively control the battery. Predictive battery charging can ensure that the battery has enough capacity free before a sunny day (so no PV power goes unused) and is sufficiently charged before an evening increased demand. The challenges here lie mainly in forecast inaccuracies and sizing: if the battery is too small or incorrectly charged, it may run empty too soon or be unable to absorb all of the surplus. Economically, it should also be considered that a storage system alone for increased self-consumption is not always financially viable—many businesses already use ~60–70% of their PV power directly, so a storage system often only brings limited additional benefits, which do not justify the costs. Nevertheless, some users appreciate increased independence from power suppliers and the long-term planning certainty of electricity costs, making them willing to invest in a storage system for maximizing self-consumption.

2. Peak shaving – smoothening expensive power peaks

Peak shaving – also known as peak shaving – is a use case primarily for commercial and industrial electricity consumers. The target is to cut short power peaks in electricity consumption, as these cause high power costs in many electricity tariffs. Specifically, grid operators calculate an annual or monthly grid fee for larger consumers based on the highest power demand (kW) recorded during a billing period. Power peaks occur, for example, when multiple large machines start simultaneously or other energy-intensive processes occur concurrently. Even if such a peak only lasts a few minutes, it can significantly increase the electricity bill. Here, the battery storage system acts as a buffer: As soon as the consumption exceeds a defined threshold, the battery automatically provides the additional power, instead of drawing it from the grid. This limits the grid load to a maximum value (the 'capped' peak), and the measured maximum grid power demand remains lower. During periods of low load, the storage system is recharged from the grid or excess PV power, ready for the next peak.

Example: If a company has a tariff with €100 per kW peak price and normally experiences peaks up to 900 kW, without storage, peak charges alone would amount to ~€90,000 per year. A battery system could limit these peaks to, say, 800 kW, reducing the billed maximum value—each avoided kW peak directly saves costs. In a practical example, a 100 kWh/50 kW battery can reduce a power peak by 50 kW, potentially saving ~€10,000 annually at a ~€200/kW capacity price.

Technical implementation and simulation: In simulations, peak shaving is formulated as an optimization problem to minimize maximum grid power. Mathematically, this can be expressed as minimizing the peak load $P_{\text{peak}}$:

$P_{\text{peak}} = \max_{t} \big( P_{\text{Load}}(t) - P_{\text{Battery Discharge}}(t) \big)$,

where $P_{\text{Load}}(t)$ is the power demand from the consumer's perspective and $P_{\text{Battery Discharge}}(t)$ is the power provided by the battery. The battery must be controlled to keep $P_{\text{peak}}$ as low as possible. Optimization is subject to constraints such as limited battery capacity and maximum discharge power. In practice, this is often represented by choosing a threshold value: e.g., "Draw a maximum of 800 kW from the grid; anything above is handled by the battery." An EMS with peak-shaving functionality continuously monitors the power and controls the battery in real-time to smooth the load curve. An intelligent algorithm is important, ensuring the battery is immediately recharged after a peak is shaved, ready for the next peak, as load forecasts can often be inaccurate, and a single "missed" peak could already significantly increase the electricity bill.

Challenges in application: Peak shaving requires an adequate sizing of the storage system for the individual load profile. The storage must provide sufficient power and energy to cover typical peaks—very high but rare peaks might not be fully shaved without an oversized (and uneconomic) storage system. Therefore, an economically optimal threshold needs to be found during storage planning. Correctly implemented, peak shaving not only reduces the electricity bill but also relieves the power grid—flatter load curves mean less need for grid expansion and reserve power plants, contributing to both climate protection and grid stability.

3. Combining both strategies – the best of self-consumption & peak shaving

In practice, battery storage systems are often designed to provide multiple benefits simultaneously to enhance their economic efficiency. A combination of self-consumption optimization and peak shaving enables synergy effects: The storage can take in solar surpluses during the day and contribute to cost reduction in the evening while simultaneously smoothing short-term power peaks. This multi-use greatly increases utility and significantly reduces the payback period of the storage. More advanced planning tools, like those from Lumera Energy, offer so-called multi-use concepts, where a battery storage system can flexibly handle multiple operating modes.

How does the combination work? Principally, both strategies can be regulated with a prioritized rule system. A simple solution is to reserve part of the battery capacity for peak shaving and use the rest for PV storage. For instance, an energy management system might set: “Always keep 30% SoC free in the battery for peak shaving; use surplus capacity for PV self-consumption”. As long as the battery is charged above 30%, it can absorb excess solar power or discharge for household use (self-consumption mode). If the state of charge drops to 30% or below, this reserve remains for peak shaving exclusively (peak-shaving mode). However, the reserved 30% of the battery often lies unused, preventing higher self-use or requiring an oversized battery, thus reducing economic efficiency.

A more modern solution involves optimization models that consider both goals in a joint objective function. In these models, both energy procurement costs (€/kWh) and power procurement costs (€/kW) are minimized together. This can be formulated as an optimization problem with the constraints of the battery (capacity, performance limits, SoC continuity) and time series for PV generation and load. Such integrated optimizations allow power costs to be lowered while simultaneously increasing self-consumption, providing both financial savings and greater supply resilience.

Challenges and practical relevance: The combined operation significantly increases energy management requirements. Conflicts need to be avoided, e.g.: Should the battery be fully charged for self-consumption on a sunny day, or should capacity be reserved because a peak might occur in the afternoon? One solution is the aforementioned reserve strategy or intelligent forecasting, which recognizes, based on planned production or historical data, whether peaks are expected on a given day. Accordingly, dynamically setting priorities is possible—for example, using full capacity for PV on a quiet Sunday, but being more cautious with discharge on a working morning to prepare for midday peaks. Multi-use systems thus require sophisticated software. Finally, economic efficiency is crucial: The acquisition of a large storage is costly, but the combination of multiple benefits creates additional revenue sources or savings potential. Research and practical projects show that only the combination of use cases (possibly coupled with additional services such as arbitrage trading or providing balancing power) truly makes a battery storage system profitable.

Conclusion

Depending on the application, battery storage can offer different advantages. For companies with demand-dependent tariffs, peak shaving is often the critical lever to reduce energy costs and simultaneously decrease grid load. The combination of both strategies allows the full potential of a storage system to be utilized: Day and night, for energy consumption and power procurement, the storage fulfills a dual role. However, there is no universal solution—each project should be individually planned. Load profiles, PV generation, tariff structures, and business processes must be analyzed to find the optimal operating strategy. With the right configuration and intelligent energy management, a battery storage system is a powerful tool: it can help significantly lower electricity costs, maximize self-consumption, and relieve the power grid—a win-win situation for operators and the energy system.