Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation

Moritz Mittelviefhaus,Giacomo Pareschi,James Allan,Gil Georges,Konstantinos Boulouchos

doi:10.1016/j.apenergy.2021.117445

Moritz Mittelviefhaus, Giacomo Pareschi + Show 3 more

Open Access

https://doi.org/10.1016/j.apenergy.2021.117445

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Abstract

Residential energy and mobility demand are responsible for a substantial share of global greenhouse gas emissions due to high dependency on fossil fuels in heating and motorized individual transport. Technology upgrades might enable cost-effective climate change mitigation in decentralized mobility-including Multi-energy Systems (MIMES). Their holistic and cross-sectoral optimization with respect to design and operation can innovatively identify tradeoffs between ecological and economical solutions, quantify potential benefits and show drawbacks over current solutions. To this end, this work provides a novel, hourly-resolved, consumer-centric, multi-objective optimization framework based on the Energy Hub concept that includes private mobility investments. It compares four distinct technology portfolios for minimal lifecycle emissions and total annualized cost when supplying residential demands to promote beneficial solutions. In a case study, consumers may modify their combustion-based heating and mobility systems (1) by switching to e-mobility (2), by switching to stationary Multi-energy Systems (3), or by switching to e-mobility and Multi-energy Systems simultaneously (4). Optimizations on 83 stochastically selected single- and multi-family buildings in St. Gallen, Switzerland, demonstrate that all three technological upgrades offer improved performance over (1): While costs decrease moderately in cost-driven optimizations, emission reductions range from 16% up to 68% when emission-driven optimizations are performed. The joint electrification of stationary and mobile assets (4) is particularly attractive and outperforms all other technology cases. Scenario (3) offers the second-best performance. While the optimal design and operation of assets depend on the technology availability, emission reduction targets, building size, and demand properties, an uncertainty analysis underpins the overall benefits of upgrades and accredits robustness to abovementioned ranking for wide techno-economic parameter variations and objectives. Pathways for further emission reductions are additionally shown, and the increased initial cost and seasonal dependence on the electricity grid are discussed as hurdles for a widespread implementation of economical and climate-friendly MIMES.

Highlights

As the use of such tools at the overlap of residential stationary and mobile asset in vestments is scarce and lacking to holistically identify optimal decision pathways for a quick decarbonization of the corresponding sectors, we propose an unprecedented consumer-centric tool to demonstrate un tapped potentials of cross-sectoral technology upgrades
The results of the optimizations, based on the method and case study presented above, are presented in the following order: Section 4.1 depicts the optimal design of converters, storage and vehicle assets for the four technology cases to highlight the relative importance of assets in the solutions
Section 4.2.2 details the impact of parameter uncertainty on the results to assess the robustness of presented claims

Summary

Introduction

Mobility- and residential building related emissions were the largest individual contributors by end-use, causing 11.9% and 10.9% of global annual CO2eq emissions respectively in 2016 [2]. In Switzerland, the mobility sector demanded 37.8% and households 27% of final energy in 2018 [3] This corre sponded to 32.4% and 16.6% of total annual greenhouse gas emissions [4]. The global energy system has been ruled by large-scale, unidirectional energy provision and distribution from centralized in frastructures due to decreasing specific costs towards larger in frastructures [5]. This paradigm is increasingly being challenged by more decentralized energy systems [6]. Reference [9] lists current barriers to decentralization and parameters influencing its future position in the energy sector

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