Reviewing local and integrated energy system models: insights into flexibility and robustness challenges

Kachirayil, Febin; Weinand, Jann Michael; Scheller, Fabian; McKenna, Russell

doi:10.1016/j.apenergy.2022.119666

Cited by 31 publications

(4 citation statements)

References 167 publications

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“…GAs are capable of generating discontinuous or concave Pareto Fronts including spatial dependencies of optimization objectives (Song and Chen, 2018a). Therefore, GAs have been used for various spatial allocation problems such as water resource management (Sahebgharani, 2016), land use planning (Schwaab et al, 2017) or energy system planning (Fischer et al, 2020;Kachirayil et al, 2022). In order to better understand trade-offs of planning olicies in multi-objective spatial allocation problems, Seppelt et al (2013) combine MO optimization with scenario approaches.…”

Section: Genetic Multi-objective Optimizationmentioning

confidence: 99%

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

Spielhofer¹,

Schwaab

Grêt‐Regamey³

2023

Environmental Science & Policy

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Section: Genetic Multi-objective Optimizationmentioning

confidence: 99%

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

Spielhofer¹,

Schwaab

Grêt‐Regamey³

2023

Environmental Science & Policy

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“…The social problem that results from encouraging electricity conservation in a community is caused by the conflict between each family's individual and communal goals, which is to consume energy as they see fit. It is important to understand the barriers to energy-efficient technology, which may lead to insight into locally based solutions for adaptability so that distributed electricity production from renewable sources may be incorporated [40]. Strategies for information dissemination account for the influence of energy community behavior.…”

Section: Energy Communitymentioning

confidence: 99%

Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community

et al. 2023

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In order to encourage energy saving and the adoption of renewable sources, this study provides a comprehensive experimental framework that integrates socioeconomic and behavioral objectives for the local energy community. The experiment aims to find out how successfully using behavioral interventions might encourage customers to save electrical energy and encourage them to adopt renewable energy, e.g., solar photovoltaic energy, in the present case. Using this method, we can calculate the causal impact of the intervention on consumer participation in the local electricity sector. The study uses consumer data on the import and export of electrical power from retailer electricity utilities at a predetermined power exchange price and a midmarket price for local energy community power transactions. The local energy community model simulates the consumption, storage, and export of 20 residential customers who, in different scenarios, are the test subjects of an empirical experiment and embrace electricity conservation and renewable energy. We address the optimization issue of calculating the power exchange cost and revenue in various scenarios and comparing them with the base case cost. The cases are built on the customers’ behavioral interventions’ empirical response. The findings demonstrate that the interaction of socioeconomic and behavioral objectives leads to impressive cost savings of up to 19.26% for energy utility customers. The policy implication is suggested for local energy utilities.

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“…Charging flexibility/patterns are rarely considered [32], but exceptions are, for instance, Cao et al [33], who compare uncontrolled and smart charging impacts on the household power demand profile. Heinisch et al [34] and Gunkel et al [35] each compare three different charging strategies.…”

mentioning

confidence: 99%

How, where, and when to charge electric vehicles – net-zero energy system implications and policy recommendations

Luh,

Kannan,

McKenna

et al. 2023

Environ. Res. Commun.

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A coordinated Charging Infrastructure (CI) strategy could accelerate the adoption of Battery Electric Vehicles (BEVs). Policymakers need to understand the tradeoffs between several types of CI developments. To support decision-makers, we apply the Swiss TIMES Energy system Model, which we extended with heterogeneous consumer segments with four trip types and several CI options. The novelty of this work lies in the interplay of such method advancements, representing BEV charging options with various CI types that can be accessed based on their location type at an hourly intraday temporal resolution. In explorative scenario analyses, we evaluate the effects of CI on car fleet deployment and their energy system implications in achieving net-zero CO2 emissions in Switzerland by 2050. Our analysis shows that the BEV share makes up 39-77% of the fleet by 2050, and each BEV needs about 5 kW total charging capacity, split into 1.6-2.6 BEVs per private charger and 18-25 BEVs per public charger. Providing overnight charging access through private home chargers or public chargers in residential areas facilitates a 12-20% increased BEV penetration compared to the reference scenario. For consumers without private home charging, improved public CI in non-residential areas increases BEV uptake by 24%. While low-power slow CI is cost-effective at home, high-power fast CI in commercial areas supports integration of solar PV. We highlight the need for coordinated CI policies and provide a variety of policy options based on our analysis and international insights.

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Reviewing local and integrated energy system models: insights into flexibility and robustness challenges

Cited by 31 publications

References 167 publications

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community

How, where, and when to charge electric vehicles – net-zero energy system implications and policy recommendations

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