Current Situation
The European electricity market is undergoing a structural transformation. The rapid build-out of solar and wind generation, driven by national renewable targets and economic advantages, is steadily increasing the share of non-dispatchable generation in the grid mix. One visible consequence is the increased occurrence of low and negative electricity prices in day-ahead spot markets: periods during which too much excess energy is available and generators must pay to offload surplus power, and conversely, consumers can be paid to consume. Such events are no longer the exception: they occur on dozens of days per year and their occurrence might continue to grow as PV capacity expands further.
Figure 1: Annual count of hours with negative (< 0 €/MWh) and low-cost (0–10 €/MWh) day-ahead electricity prices in the Austrian EPEX market, 2021–2026. Source: ENTSO-E Transparency Platform. 2026 data covers Jan–May only.
For energy-intensive consumers, this situation creates both a risk and an opportunity: energy procurement strategies locked into flat-rate tariffs or inflexible load profiles miss out on the low-price windows. However, consumers with controllable loads - electric vehicle chargers, heat pumps, industrial processes - or on-site energy storage can actively exploit price signals to minimize their electricity costs.
With the energy system optimization framework LEC ENERsim, scenarios like these can be set up, parametrized, and evaluated with high temporal resolution and full economic transparency. ENERsim constructs a linear model of the complete energy system - capturing generation, storage, flexible demand, and grid interaction - and finds the cost-optimal dispatch and investment strategy in a single run.
This article presents a representative use case: a virtual Austrian company with significant electricity demand, a rooftop PV installation, and a fleet of electric vehicles. Starting from a baseline configuration, two strategies are evaluated in terms of annual costs step by step: First, a stationary battery system, and second, an optimized EV charging.
The Baseline System
Figure 2: The LEC ENERsim user interface showing the baseline system topology: PV, grid connection, unmanaged EV charging, load, and a dump/curtailment component.
Load and PV Generation
The model company is characterized by an annual electricity demand of 1,000 MWh/year — representative of a mid-sized industrial or commercial operation such as a production facility, logistics hub, or research campus. All electricity is procured from the grid at dynamic spot-market pricing based on the Austrian day-ahead market (EPEX Spot), supplemented by applicable grid fees. On-site generation is provided by a 700 kW rooftop photovoltaic system.
PV profiles are generated using the PVGIS tool provided by the Joint Research Centre. PVGIS delivers hourly irradiance and temperature data for any location and translates these into AC power output for a defined PV installation.
Load profiles are based on the BDEW standard load profiles (Standardlastprofile) for business and industrial consumers, scaled to the target annual demand. Realistic intra-day and intra-week variability is introduced by superimposing a stochastic noise component.
ENERsim also supports direct import of custom measured load profiles from CSV/XLSX files, allowing the model to be calibrated to actual consumption data.
Excess PV generation that cannot be consumed on-site or stored can also be fed to a curtailment component, which models the scenario where surplus generation is discarded rather than sold, for example during negative price events.
In addition to the spot energy price, grid-connected consumers usually pay regulated network tariffs and taxes. For this study, the following components are applied, consistent with the rates published by the Austrian energy regulator E-Control for the relevant grid level:
- Power component (Leistungspreis, LP): 60 EUR/kW/year, charged on the annual peak load.
- Energy component (Arbeitspreis, AP): 1.9 ct/kWh — a charge on all energy drawn from the grid.
- Network loss levy (Netzverlustentgelt): 0.12 ct/kWh — a small additional charge covering transmission and distribution losses.
- Electricity fee (Elektrizitätsabgabe): 0.82 ct/kWh.
Electric Vehicle Fleet
The virtual company is assumed to operate a fleet of business electric vehicles that charge on-site. The charging demand profile is modeled using a bi-Gaussian arrival distribution that reflects realistic usage patterns: a morning peak when employees arrive and connect their vehicles and an evening peak when vehicles return from off-site trips. AC-Chargers are rated at 11 kW and 22 kW, and the total number of charging sessions is calibrated to reflect a representative SME fleet.
In the baseline configuration, EV charging is unmanaged. Vehicles begin charging as soon as they connect, representing the default behavior of most existing charging infrastructure.
Baseline Results
Given the parametrized system and hourly electricity prices of 2025, ENERsim calculates all energy flows and evaluates cost components for a complete year.
Figure 3: Energy flows for the baseline configuration, showing PV generation, grid import, load consumption, and dump flows.
The Sankey diagram shows how PV generation delivers a high share of the total demand, with the remainder drawn from the grid. During periods of high PV yield and low on-site consumption, surplus generation flows to the feed-in grid and the dump component.
Figure 4: Annual cost breakdown and distribution for the baseline configuration.
For the baseline system, ENERsim computes an annual total cost of approximately 120.9 kEUR. The cost structure is dominated by energy costs, which account for roughly 70 % of total costs, while fees — in particular the power component — contribute the remaining ~30 %. This split immediately reveals cost optimization potentials: reducing the peak loads and aligning consumption with low price periods.
The figures presented throughout this article are illustrative by design. They demonstrate ENERsim's modeling and optimization capabilities, not general benchmarks for any particular facility. Real-world results depend strongly on:
- Electricity price assumptions overall price levels and dynamics are highly year- and scenario-dependent
- Load profile shape — strongly influences PV self-supply, grid consumption, and grid fees
- PV system yield — location, orientation, shading, and panel degradation affect self-consumption ratios
- EV fleet composition — charging power levels, fleet size, and usage patterns determine the flexibility potential
- Investment cost assumptions — here a 200 EUR/kWh estimate for the battery is used, based on 2025 market values (see below)
Use Case 1: Battery Storage
System Configuration
The first scenario introduces a stationary battery storage system into the energy system. The battery can charge from surplus PV generation as well as from the grid during low- or negative-price periods, and discharges to cover demand during high-price periods.
Figure 5: System topology with the battery storage unit added. The optimizer determines both the optimal capacity and hourly dispatch strategy.
Critically, the battery capacity is not specified in advance. Instead, ENERsim's optimizer simultaneously determines the optimal storage size and the optimal hourly dispatch over the full year — balancing the annualized investment cost against the operational savings.
The battery is parametrized with:
- Round-trip efficiency (AC-to-AC): 95 %
- C-rate: 0.25 C (4-hour storage)
- Technical lifetime: 15 years (~4,000–5,000 full cycles)
- Investment cost: 200 EUR/kWh (incl. inverter, installation)
Battery pack prices have declined by more than 90 % over the past decade and continue to fall. According to BloombergNEF's 2025 Battery Price Survey, the global average lithium-ion battery pack price reached 108 USD/kWh in 2025 — a new record low. System-level costs for stationary storage installations (including power electronics, installation, and grid connection) remain higher than pack-only prices, typically by a factor of 1.5–2.5, but are declining in parallel. The 200 EUR/kWh figure used in this study is therefore a mean estimate for a commercial-scale installation in the 1–2 MWh range in 2025. Projects in less favorable conditions or requiring additional civil works may be higher, projects benefiting from volume procurement or favorable site conditions may be lower.
Optimized Results
ENERsim's optimization selects a battery capacity of 1,300 kWh, shifting a meaningful share of daily demand from high-price to low-price periods and to achieve significant peak shaving.
Figure 6: Energy flows for the battery storage configuration. The battery absorbs surplus PV and low-cost grid energy.
Figure 7: Annual cost breakdown and distribution for the battery storage configuration, including annualized battery depreciation.
The battery storage scenario yields an annual operating cost of approximately 72 kEUR (excluding battery depreciation) — down from 120.9 kEUR in the baseline. When the annualized investment cost (depreciation over 15 years) is included, the total annual cost rises to approximately 89 kEUR. This means:
- Operational savings vs. baseline: ~49 kEUR/year: a reduction of 40 % in operating costs driven by peak shaving, increased PV self-consumption, and opportunistic charging during negative-price periods
- Total investment: ~260 kEUR (1,300 kWh × 200 EUR/kWh)
- Simple payback period: approximately 5 years — well within the battery's 15-year technical lifetime
The power component of the grid fee is significantly reduced, as the battery also discharges precisely during peak demand periods to keep the grid import power flat.
Use Case 2: Demand-Side Flexibility — Managed EV Charging
The Flexibility Concept
The second scenario introduces a fundamentally different approach: demand-side flexibility through managed EV charging. No new hardware is required — the existing charging infrastructure is controlled by the optimizer to shift charging loads in time, exploiting price signals and PV production without reducing the total amount of energy delivered to the vehicle fleet.
A vehicle that plugs in at 08:00 and departs at 16:00 must receive a certain amount of energy by the time it leaves, but the timing of that charging is flexible within the parking window. Similarly, a vehicle arriving in the evening and departing the next morning can be charged at any point overnight. ENERsim models this flexibility by specifying:
- Morning arrivals: vehicles connect in the morning and can charge at any point before their planned evening departure.
- Evening arrivals: vehicles connect in the evening and can charge at any point before their planned next-morning departure.
The optimizer then shifts the charging schedule to low-price or high PV yield periods while satisfying all departure constraints. This requires no changes to the hardware and could in practice be implemented via a smart charging controller communicating with the existing charger infrastructure.
Optimized EV Charging Profile
Figure 8: Comparison of the unmanaged baseline charging profile (red) and the optimized managed charging profile (blue) for a representative week in March 2025. The optimizer shifts charging away from high-price morning peaks toward midday PV surplus and low-price overnight periods.
Figure 8 shows the transformation: the unmanaged profile features morning and evening peaks driven by the arrival distribution. The optimized profile spreads charging across the day, concentrating it during periods of high PV output and low spot prices, including overnight windows. Total energy delivered to the fleet is identical in both cases; only the timing changes.
Results with Managed Charging
Figure 9: Energy flow Sankey diagram for the demand flexibility (managed EV charging) configuration.
Figure 10: Annual cost breakdown and distribution for the demand flexibility configuration.
With managed EV charging enabled, annual total costs fall to approximately 105.5 kEUR — a saving of ~15.4 kEUR/year compared to the 120.9 kEUR baseline, representing a 13 % reduction. Crucially, this saving is achieved without any additional hardware investment: the only requirement is a software-based control layer that communicates charging schedules to the existing chargers based on the optimizer's output.
The savings arise from two complementary effects:
- Spot-market arbitrage: shifting charging to low or negative price windows or high PV production directly reduces energy costs
- Peak demand reduction: distributing charging more evenly reduces the peak grid power, lowering the power-component grid fee
Summary
The three configurations demonstrate the potential of sophisticated energy management strategies:
| Configuration | Annual Cost (kEUR) | vs. Baseline | Key Investment |
|---|---|---|---|
| Baseline — PV + grid + EV charging (unmanaged) | 120.9 | — | None |
| Battery storage (1,300 kWh, optimized) | 89.0 | −26 % | ~260 kEUR (≈5 yr payback) |
| Demand flexibility — managed EV charging | 105.5 | −13 % | Minimal (control layer only) |
Battery storage delivers the larger absolute saving and carries a well-defined payback period well within the system's technical lifetime; managed EV charging is a high-return, low-risk entry point requiring only a control layer. As the two strategies are complementary — storage arbitrage and flexible load alignment — deploying both simultaneously would be expected to yield even stronger savings.
Outlook
The two use cases presented here are just two examples. LEC ENERsim's modular component library and flexible topology make it straightforward to extend the analysis in many directions, including:
- Heat pumps and thermal storage: operate a heat pump as a controllable electrical load, charging a thermal store during low-price hours, jointly optimized with PV and battery
- Vehicle-to-grid (V2G): bidirectional chargers allow EV batteries to discharge back into the microgrid, turning the fleet into a distributed energy storage
- Wind and hydro: model wind turbines or run-of-river hydro, and use battery or demand-side flexibility in a similar way
- Green hydrogen production: electrolyzers absorb PV or wind generation, converting it to hydrogen for industrial use or mobility
- Grid congestion avoidance: model dynamic export/import limits on the grid connection to assess the impact of grid constraints (see image below)
- Multi-vector systems: optimize electricity, heat, hydrogen, and cooling simultaneously in a single integrated system using ENERsim's multi-grid architecture
Figure 11: Illustration of grid congestion avoidance: battery storage and managed demand respond to a grid limit, allowing to better use available capacities.
