LuSim relies on a GPU based 3D modelling approach that represents photovoltaic systems and their environment explicitly in space. Terrain, structures, modules, and surrounding elements are modelled directly rather than approximated through simplified geometric assumptions.
This approach enables spatially and temporally resolved evaluation of irradiance, shading, and energy yield. It provides the physical foundation shared by all LuSim services, ensuring consistency across studies while allowing the modelling detail to be adapted to the objectives of each project.
LuSim enables multiparameter optimisation through batch simulations that evaluate families of configurations under identical physical assumptions and boundary conditions.
Multiple parameters can be varied simultaneously, either through structured grids, targeted scenarios, or exploratory combinations. Each configuration is simulated using the same 3D representation of geometry, shading, irradiance exchange, and system losses, ensuring that differences in results are attributable to design choices rather than modelling inconsistencies.
This approach allows large design spaces to be explored efficiently while maintaining physical coherence across simulations.
A key requirement for meaningful optimisation is consistency across all evaluated scenarios. In LuSim, every configuration within a batch simulation relies on the same underlying modelling approach for irradiance, shading, and PV energy yield.
Direct irradiance shading is evaluated through geometric visibility, while diffuse and reflected components are treated using high spatial resolution 3D view factors. Electrical performance and losses are computed using validated photovoltaic models applied consistently across all scenarios.
This ensures that technical and economic comparisons between configurations remain physically meaningful, even when multiple parameters vary simultaneously.
Multiparameter optimisation in LuSim is not limited to maximising annual energy production. Financial performance indicators can be explicitly integrated as optimisation objectives.
Key metrics such as levelised cost of energy, internal rate of return, and net present value can be computed for each simulated configuration based on energy yield results, cost assumptions, and financial parameters.
This allows optimisation exercises to target profitability rather than energy output alone. In many cases, the configuration that maximises annual production is not the one that maximises economic value. LuSim makes these trade offs explicit by linking technical simulation outputs with financial evaluation.
LuSim supports optimisation studies that take into account temporal electricity price signals rather than relying solely on annual energy totals.
By combining time resolved PV production profiles with spot market prices or other price structures, simulations can be used to evaluate revenue rather than energy alone. This makes it possible to explore design choices that favour production during high price periods, even if they reduce total annual yield.
Such analyses are particularly relevant in markets with high price volatility or increasing penetration of variable renewable energy, where the timing of production has a direct impact on profitability.
Multiparameter optimisation is not limited to identifying a single optimal configuration. In practice, it is often more important to understand sensitivities and trade offs between competing objectives.
LuSim supports the identification of parameters that have the strongest influence on energy yield, financial performance, risk exposure, or robustness to uncertainty. It also enables analysis of trade offs, for example between capital expenditure and operational flexibility, energy maximisation and revenue maximisation, or system complexity and financial return.
This information supports informed design decisions, especially in projects where constraints are driven by regulatory, financial, or site specific considerations.
Multiparameter optimisation can also include battery energy storage systems and their operational strategies.
LuSim can be used to evaluate different combinations of PV system design and storage capacity, as well as alternative operating strategies such as self consumption maximisation, peak shaving, price arbitrage, or grid support.
By simulating PV production, storage behaviour, and electricity prices together, optimisation exercises can assess how storage influences energy flows, revenues, and financial performance indicators.
This allows design questions related to storage sizing, control strategy, and economic value to be addressed within the same modelling framework as PV system optimisation.
Multiparameter optimisation provides the foundation for robust uncertainty assessment. By exploring plausible ranges of design parameters, operating strategies, and price assumptions, LuSim helps quantify how variability in inputs propagates to both energy and financial results.
The outputs of batch simulations can be used directly to support exceedance probability analysis, where distributions of indicators such as energy yield, revenue, or financial KPIs are derived from physically and economically meaningful scenarios.
This ensures that exceedance metrics reflect actual design and market variability rather than abstract statistical assumptions.
Rather than prescribing a single optimal solution, multiparameter optimisation with LuSim supports structured decision making. It provides quantitative insight into how technical design, operational strategy, and market conditions interact to influence performance and profitability.
This service is particularly suited to early stage design, feasibility studies, and comparative assessments, where understanding the design and decision space is more valuable than converging prematurely on a single configuration.
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