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ENPhotovoltaic system performance is rarely governed by a single design parameter. In many projects, energy yield, losses, and economic performance depend on combinations of interacting variables such as geometry, spacing, height, orientation, ground properties, tracking strategy, storage capacity, and surrounding environment.
In complex PV systems, these interactions cannot be captured reliably through isolated sensitivity checks or single deterministic simulations. Multiparameter optimisation is therefore required to explore design spaces systematically and to understand how technical choices translate into both energy and financial outcomes.
LuSim has been developed to support this type of analysis within a consistent 3D modelling framework that links physical modelling with economic performance indicators.
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 using 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.
Meaningful optimisation requires strict 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 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 account for 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 enables the exploration of design and operational choices that favour production during high-price periods, even if total annual yield is reduced.
Such analyses are particularly relevant in markets with high price volatility or increasing penetration of variable renewable energy.
Multiparameter optimisation can include battery energy storage systems and their operational strategies.
LuSim can be used to evaluate combinations of PV system design, storage capacity, and control strategies such as self-consumption maximisation, peak shaving, price arbitrage, or grid support. By simulating PV production, storage behaviour, and electricity prices together, optimisation studies assess how storage influences energy flows, revenues, and financial performance.
This allows questions related to storage sizing, operation, and economic value to be addressed within the same modelling framework as PV system optimisation.
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, and robustness to uncertainty. It also enables analysis of trade-offs such as capital expenditure versus operational flexibility, energy maximisation versus revenue maximisation, or system complexity versus economic return.
This information supports informed design decisions, particularly in projects constrained by regulatory, financial, or site-specific factors.
By exploring plausible ranges of design parameters, operating strategies, and price assumptions, multiparameter optimisation in LuSim provides a natural foundation for uncertainty and exceedance analysis.
The outputs of batch simulations can be used directly to derive distributions of energy, revenue, and financial indicators based on physically and economically meaningful scenarios. This ensures that exceedance metrics reflect real design and market variability rather than abstract statistical assumptions.
Rather than prescribing a single optimal solution, LuSim supports structured decision-making by clarifying trade-offs, sensitivities, and risks. This makes it particularly well suited to early-stage design, feasibility studies, and comparative assessments, where understanding the decision space is more valuable than converging prematurely on a single configuration.
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