Portfolio Modeling & Electricity Price Forecast

Prepared by Jesse Montano, Herve Pare, & Brian Rahman P.E.


This case study delves into the application and the use of ZGlobal eGrid Analytical process to conduct energy portfolio modeling, optimization, and the computation of both short-term and long-term electricity costs to consumers or revenue to energy suppliers for various grid locations. These calculations are meticulously derived from input assumptions employing Production Cost modeling techniques. These techniques represent a set of optimization procedures designed to predict the electricity costs necessary to meet specific demand scenarios. The forecasted electricity cost spans a time horizon ranging from hourly assessments to projections extending up to a 20-year horizon.


In energy management and grid optimization, ZGlobal eGrid Analytics emerges as an indispensable tool, facilitating the intricate processes of energy portfolio modeling and electricity price forecasting. This case study elucidates the core methodologies and key considerations employed in applying this robust tool for critical decision-making within the energy sector.

Production Cost Modeling Techniques:

Production cost modeling, employed within the ZGlobal eGrid Analytics framework, encompasses a suite of optimization procedures aimed at anticipating the electricity costs imperative for meeting distinct demand scenarios. These models are characterized by their ability to forecast electricity costs across diverse timeframes, ranging from hourly to a considerable 20-year horizon.

Production cost models, founded on deterministic methodologies, have been the cornerstone of energy forecasting and optimization for several decades. They rely upon a well-defined set of assumptions underpinning the modeling results’ accuracy and precision. The deterministic approach primarily comprises the following key components:

  1. Accurate Generation Modeling: This entails the precise modeling of various generation capabilities including imports and exports of energy, encompassing generation capacity factors, heat rates, ramp rate variables, incremental costs, startup times, energy storage modeling and other pertinent factors. Ensuring the accuracy of these parameters is paramount for accurate electricity cost projections.
  2. Demand Characteristics: The comprehensive modeling of demand characteristics forms an essential component of the production cost modeling process. Anticipating fluctuations in demand patterns and their impact on electricity costs is integral to the overall modeling framework.
  3. Constraint Modeling: A critical aspect of production cost modeling is the consideration of diverse constraints that govern the energy grid’s operation. These constraints encompass transmission limitations, losses, network topology, fuel availability, pollution allowances, transmission rates, and associated costs. Incorporating these constraints into the model ensures that the forecasts remain cognizant of the real-world operational and environmental factors that influence electricity costs.
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