Zero Emission Neighborhoods Research Articles

Optimal investment in the energy system of Zero Emission Neighborhoods considering the refurbishment of the building stock

To increase the impact that Zero Emission Neighbourhoods (ZEN) can have in the effort to decrease CO2 emissions, the refurbishment of the existing building stock is a parameter that should be considered. The existing literature contains work on optimization models for the energy system of neighbourhoods taking into account emissions but fails to account for the refurbishment of buildings. This paper addresses this option and presents an optimization model for designing a cost-optimal energy system of a ZEN in the context of existing buildings. The model is presented and used in a case study in Norway and compared to a case with linearized binaries. A sensitivity analysis is performed on the cost of refurbishment. With the original refurbishment cost assumptions, it is not chosen by the optimization, contrary to the hydronic. The system relies mainly on PV, solar thermal collectors (ST), a biogas engine, a battery and heat pumps (HP) and heat storage. From 50% of the original refurbishment cost, it is chosen, and the system does not have a biogas engine and a heating grid anymore, but a much bigger battery and more heating technologies inside the buildings. With linearized binaries, the investments are similar to the case with 50% refurbishment cost, but the value of the linearized binaries cannot be used to indicate the share of building to refurbish.

Impact of the [formula omitted] factor of electricity and the external [formula omitted] compensation price on zero emission neighborhoods’ energy system design

Existing literature on Zero Emission Neighborhoods (ZENs) and Buildings (ZEBs) only allow for reaching the zero emission target locally. This paper evaluates the impact of allowing to buy CO2 compensation to reach that target in the design of ZENs. This is motivated by questions regarding the relevance of investing in local renewable production (mainly from PV) in a power system dominated by renewable hydropower. Further, it contributes to the existing literature regarding ZENs and ZEBs by highlighting the importance of the choice of the CO2 factor of electricity for the design of ZENs’ energy system.A case study illustrates the impact of those choices on the resulting energy system design using the existing ZENIT model. Three CO2 factors for electricity are used in the case study: a yearly average CO2 factor for Norway (18 gCO2∕kWh), an hourly average CO2 factor for Norway and a yearly average European factor (at 132 gCO2∕kWh). The energy system design of the ZEN is little affected when using hourly CO2-factors compared to yearly average factors, while the European factor leads to less investment in PV. Hourly marginal CO2 emission factors are also investigated using three accounting methods. There large differences in energy system design and emissions depending on where the factor is applied. The price of external compensation is varied between 0–2000 €/tonCO2. A lower price of external CO2 compensations mainly reduces the amount of PV investment. Allowing the purchase of CO2 compensations at 250 €/tonCO2 could reduce the total costs by more than 10%.

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

This paper investigates the use of clustering in the context of designing the energy system of Zero Emission Neighborhoods (ZEN). ZENs are neighborhoods who aim to have net zero emissions during their lifetime. While previous work has used and studied clustering for designing the energy system of neighborhoods, no article dealt with neighborhoods such as ZEN, which have high requirements for the solar irradiance time series, include a CO2 factor time series and have a zero emission balance limiting the possibilities. To this end several methods are used and their results compared. The results are on the one hand the performances of the clustering itself and on the other hand, the performances of each method in the optimization model where the data is used. Various aspects related to the clustering methods are tested. The different aspects studied are: the goal (clustering to obtain days or hours), the algorithm (k-means or k-medoids), the normalization method (based on the standard deviation or range of values) and the use of heuristic. The results highlight that k-means offers better results than k-medoids and that k-means was systematically underestimating the objective value while k-medoids was constantly overestimating it. When the choice between clustering days and hours is possible, it appears that clustering days offers the best precision and solving time. The choice depends on the formulation used for the optimization model and the need to model seasonal storage. The choice of the normalization method has the least impact, but the range of values method show some advantages in terms of solving time. When a good representation of the solar irradiance time series is needed, a higher number of days or using hours is necessary. The choice depends on what solving time is acceptable.

A life‐cycle assessment model for zero emission neighborhoods

AbstractBuildings represent a critical piece of a low‐carbon future, and their long lifetime necessitates urgent adoption of state‐of‐the‐art performance standards to avoid significant lock‐in risk regarding long‐lasting technology solution choices. Buildings, mobility, and energy systems are closely linked, and assessing their nexus by aiming for Zero Emission Neighborhoods (ZENs) provides a unique chance to contribute to climate change mitigation. We conducted a life‐cycle assessment of a Norwegian ZEN and designed four scenarios to test the influence of the house size, household size, and energy used and produced in the buildings as well as mobility patterns. We ran our scenarios with different levels of decarbonization of the electricity mix over a period of 60 years. Our results show the importance of the operational phases of both the buildings and mobility in the neighborhood's construction, and its decline over time induced by the decarbonization of the electricity mix. At the neighborhood end‐of‐life, embodied emissions then become responsible for the majority of the emissions when the electricity mix is decarbonized. The choice of functional unit is decisive, and we thus argue for the use of a primary functional unit “per neighborhood,” and a second “per person.” The use of a “per m2” functional unit is misleading as it does not give credits to the precautionary use of floor area. To best mitigate climate change, climate‐positive behaviors should be combined with energy efficiency standards that incorporate embodied energy, and absolute threshold should be combined with behavioral changes.