• Energy Research

This paper addresses the following question: How can smart energy management system (SEMS) influence the residential electricity consumption at both individual household and national level? First, we developed an hourly optimization model for individual households. The energy cost of an individual household is minimized under given assumptions on outside temperature, radiation, (dynamic) electricity price, and feed-in tariff. By comparing the optimization to the reference scenario, we show the impact of SEMS on grid-electricity consumption and photovoltaic (PV) self-consumption at the individual household level. Second, to aggregate the results to the national level, we constructed a detailed building stock taking Austria as an example. By aggregating the results of 2112 representative households, we investigate the impact of SEMS in the residential building stock on the national electricity system. As a result, we found that for individual single-family-houses (SFHs) with PV (no battery) and heat pump adoption, SEMS can significantly reduce the grid-electricity consumption up to 40.7% for a well-insulated building. At the national level we found that, for the buildings with 5 kWp PV but without hot water tank or battery storage, SEMS can still reduce the grid-electricity consumption by 7.4% by using the building mass as thermal storage.

The electrification of households’ heating systems will lead to an increase in the electricity demand, which will necessitate additional investments in the grid infrastructure. The interaction with other technologies, including PV, batteries, electric vehicles (EVs), and home energy management systems (HEMSs), further complicate the situation. In this study, we analyze the following question: How will prosumaging households, who consume, produce and manage their energy consumption with HEMS, impact the future reinforcement costs of the electricity distribution grid? We conducted case studies for two urban areas, Murcia in Spain and Leeuwarden in The Netherlands. First, by developing scenarios on the uptake of electrified heating systems, PV installations, battery storage, EVs and HEMSs, the energy demand of each building is modeled for the two areas under different scenarios. Then, the buildings’ electricity load profiles were provided to a second model, to calculate the necessary distribution grid infrastructure to cover this demand on a granular spatial level. Results show that low voltage lines and transformers will need significant investments, especially in the regions where a high share of conventional heating systems are replaced by heat pumps but also in regions where the aggregate electricity peak demand is reduced.

To achieve the Paris Agreement goals, all countries need to implement two central strategies: i) enhancing energy efficiency (EE) and (ii) decarbonizing the remaining energy supply and demand. Scenarios with different focuses and assumptions have been derived to map this development until 2050. In this context, the newTRENDs project prepares the analytical basis for a "2050 Energy Efficiency Vision" by considering New Societal Trends in energy demand modeling.

The future increase in electric consumers in households, especially electric vehicles and heat pumps, as well as the rising solar power installation, allows for a certain degree of grid-independency and cost savings through self-generated electricity. Both the use of self-generated electricity and cost savings may be increased through the implementation of smart energy management systems at home, which may also have a beneficial impact on electric vehicle sales. In this paper, we combine two models that optimize the energy consumption at household level and a market diffusion model for alternative fuel vehicles to address this aspect for Germany in 2030. Results show that the use of smart energy management systems can result in savings of up to €900-€1200 per year for households with heat pumps, photovoltaics, and electric vehicles, depending on the daily and annual mileage. The results of our analyses indicate that additional battery storage systems are unlikely to be cost-effective with electric vehicles in place. Market shares of battery electric vehicles can be increased, especially for households with small vehicles, where price sensitivities are highest. However, smart charging without solar power only returns small savings and has no impact on electric vehicle market shares.

AbstractEnergy Efficiency First (EEF) is an established principle for European Union (EU) energy policy design. It highlights the exploitation of demand-side resources and prioritizes cost-effective options from the demand-side over other options from a societal cost-benefit perspective. However, the involvement of multiple decision-makers makes it difficult to implement. Therefore, we propose a flexible decision-tree framework for applying the EEF principle based on a review of relevant areas and examples. In summary, this paper contributes to applying the EEF principle by defining and distinguishing different types of cases — (1) policy-making, and (2) system planning and investment — identifying the most common elements, and proposing a decision-tree framework that can be flexibly constructed based on the elements for different cases. Finally, we exemplify the application of this framework with two example cases: (1) planning for demand-response in the power sector, and (2) planning for a district heating system.

Given the important role of the carbon market in fighting against global warming, the impact of information on the efficiency of the scheme is a critical issue for both its designers and the central authority. At least two aspects of information are worthy of attention. First is the incompleteness of information, with which the firms make decisions to minimize their abatement cost and maximize their profit. Second is the mechanism of information transmission. Based on an agent-based framework we established in our previous work, we explicitly depicted the first aspect and analysed its impact on firms’ decision-making and consequent market results. In this paper, we focus on the second aspect, transmission mechanism of information, which is depicted as an observation network among firms. The basis in reality is that the firms in the carbon market are usually from different industries or areas, and it is relatively easier to observe the conditions of firms from the same industry or area, corresponding to neighbours in the network. Four scenarios are considered, including no network, regular network, random network, and small-world network. We find that the existence of an observation network has a significant influence on the market results.