Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

Abstract

The global energy system is undergoing a major transformation. Renewable energy generation is growing and is projected to accelerate further with the global emphasis on decarbonization. Furthermore, distributed generation is projected to play a significant role in the new energy system, and energy models are playing a key role in understanding how distributed generation can be integrated reliably and economically. The deployment of massive amounts of distributed generation requires understanding the interface of technology, economics, and policy in the energy modeling process. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite ™ , which addresses this need effectively. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the accessibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby significantly boosting the rate the renewable energy deployment. Lastly, but equally importantly, this computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

Optimal Planning of Co-Located Wind Energy and Hydrogen Plants: A Techno-Economic Analysis

“…The techno-economic analysis for the sector-coupled wind-hydrogen system is performed by adopting REopt, an open-source techno-economic tool developed by NREL [12]. An overall framework of the REopt techno-economic analysis for the integrated wind and hydrogen system is shown in Fig.…”

“…Figure 1: Techno-economic analysis framework for the integrated wind and hydrogen system (modified from [12]) efficiency, and other performance parameters of the wind energy system to estimate the power generation. For modeling the hydrogen production, the electrolyzer technology takes electricity and a feed stock (e.g., water) to produce hydrogen.…”

“…The detailed mathematical formulations on the objective and constraints could be found in Ref. [12]. A mixed-integer linear program (MILP) is formulated for this problem, and the FICO Xpress solver is adopted to solve the optimization problem to determine the optimal component selection, sizing, and dispatch strategy of technologies chosen from a candidate pool, such that loads are met at every time step at the minimum life cycle cost.…”

Probabilistic Resilience of DER Systems - A Simulation Assisted Optimization Approach

“…The deterministic MILP formulation recommends the optimal technology mix, size, and dispatch, and the resulting project lifecycle economics. Further details are presented in [6] and [7].…”

Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design

“…To support these primary use cases, the URBANopt modules are divided into Building Core Modules [36], Grid Interactivity Modules and District Energy System Modules. As shown in Figure 2, the modules map user inputs to various simulation engines, tools and libraries such as OpenStudio, EnergyPlus, Open Distribution System Simulator (OpenDSS) [41], the Renewable Energy Integration and Optimization (REopt™) Lite tool [42] and the Modelica Building Library to analyze components of a district or community. To make use of their modularity and encourage collaboration, the URBANopt modules have clearly defined inputs and outputs.…”