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Computational LDES Deploy

Peniel envisions a cross-cutting analytical framework that can support faster deployment of LDES technologies currently at commercial stage. Peniel enables the use of a combination of decision support, scenario modelling, technology characterisation and performance data, data generated from physics-based models and digital twins, market data, industry insights data, and field deployment data. 


The field deployment data is especially crucial as it enables tools which predict authentic life in the field. Providing this critical end-to-end data is therefore key to unlocking the power of this transformational paradigm. Key technologies which form part of our LDES deploy toolset to directly accelerate routine, commercial energy storage deployment can be noted below.  

LDESGoZero® - B

LDESGoZero® - B [LGZ-B] is an AI/ML Engine for the electric power sector. LGZ-B® uses a set of LDES input assumptions to model the evolution and operation of LDES within generation, transmission, and end- use demand technologies. The LGZ-B® Engine intuitively enables the visualisation of the triad of LDES Deployment i.e. Technology, Policy & Innovative Finance for robust scenario planning. The engine is designed to argument users from both the power sector and other specific stakeholders [ for more details on specific stakeholders, learn more].

The LGZ-B® engine leverages a modular structure for optimum user flexibility. LGZ-B® is built from three separate but interrelated modules: the supply, the demand and operation modules. The supply module solves for the cost-minimising levels of power sector investment and operation. The demand module maximises the levels of end-use device investment and operation. The operations module calculates key parameters for assessing the value of Solar/Wind and LDES. The modules are executed iteratively until a supply-demand equilibrium for electricity is achieved. 

How the The LGZ-B® supply side engine was built. We have extended all our efforts to represent all existing generating units, planned future builds, and endogenous new capacity within each region of our focus. On the demand side, the model represents the existing stock of end-use devices as well as an endogenous composition of new device purchases for residential consumers.

Scenario 2050

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First the US, then the rest of the world

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We are currently only able to provide this service to US and Costa Rica, with plans for global coverage in the near future. Stay tuned to the latest updates and coverage. 

Scenario 2050

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System-wide Optimisation

LGZ®-B takes a system-wide, least-cost perspective that does not necessarily reflect the perspectives of individual decision makers, including specific investors, regional market participants, or corporate or individual consumer choice; nor does it model contractual obligations or noneconomic decisions. In addition, like other optimization models, LGZ®-B finds the absolute (deterministic) least-cost solution that does not fully reflect real distributions or uncertainties in the parameters; however, the heterogeneity resulting from the high spatial resolution of LGZ®-B mitigates this effect to some degree.

How The Model Works

LGZ®-B combines two optimization modules with a simulation module. One of the two optimization modules represents electricity supply, and the other represents end-use energy service demand. The simulation model uses a dispatch algorithm and multiple years of chronological hourly wind, solar, and load data to estimate the contribution of Solar/Wind + LDES units to capacity and the level of curtailment for Wind/Solar units. Though both optimization modules are described here, only the supply module is used for most LGZ®-B analysis [i.e., the demand module is typically turned off for most runs].


The model can be run sequentially, intertemporally, or using a sliding window. Figure 1 illustrates how the modules interact when the model is run in sequential solve mode. Within an iteration, the supply and Wind/Solar modules are run in sequence for each model solve year. For a given model year t, the supply module, which has been provided with a set of inputs dependent on the results from previous model years, is solved and a subset of the outputs are passed along to the Wind/Solar + LDES module.


Using these inputs, the module then calculates the capacity value of VRE and storage, and curtailment rates for VRE units, which are then applied to the next model year solve (t+1).5 After this recursive procedure is executed for all model years, the resulting electricity prices for all model years are passed to the demand module. Based on those prices, the demand module solves and exports new values for load, which the supply module then uses the in the next iteration. This process repeats until convergence (i.e., until the results of successive iterations are sufficiently close)

Combined Heat Power [CHP]

LGZ®-B contains a comprehensive database of combined heat and power installations throughout the world. We tracks installations of all sizes, from large industrial systems that are hundreds of megawatts in size to small commercial microturbine and fuel cell systems that are tens of kilowatts. The database can be used within our application to provide a high resolution LDES Deployment 2050 scenario planning environment. 

For more details, contact one of our product specialists

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