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Energy ecosystems are under a significant transition. Local flexibility marketplaces (LFM) and platforms are argued to have significant potential in contributing to such a transition. The purpose of this study was to answer the following research question: how do market conditions and stakeholders shape emerging LFM platform governance choices? We approached this objective with an exploratory single-case study by conducting ten semi-structured interviews with key stakeholders in the Finnish energy ecosystem. The results of the content and pattern analyses revealed the key challenges to LFM implementation such as the current regulatory treatment of flexibility, high costs of gadget installations, and ensuring sufficient liquidity in the market. In addition, we also demonstrated that despite such barriers, the Finnish ecosystem is largely pragmatic about LFMs’ in its midst. All in all, we contributed to the non-technological streams of LFM literature by developing an exhaustive framework with four distinctive dimensions (i.e., ecosystem readiness, value-creation logic, platform architecture and governance, platform competitiveness) for LFM development, which helps academics, practitioners, and policy-makers to understand how novel platforms emerge and develop.

Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this paper is to solve the challenges and find opportunities caused by global warming by utilizing seabed sediment energy as a renewable heat source. Measurement data of water and air temperature were analyzed, and correlations were established for the sediment temperature data using Statistical Analysis System (SAS) Enterprise Guide 7.1. software. The analysis and provisional forecast based on the autoregression integrated moving average (ARIMA) model revealed that air and water temperatures show incremental increases through time, and that sediment temperature has positive correlations with water temperature with a 2-month lag. Therefore, sediment heat energy is also expected to increase in the future. Factor analysis validations show that the data have a normal cluster and no particular outliers. This study concludes that sediment heat energy can be considered in prominent renewable production, transforming climate change into a useful solution, at least in summertime.

Climate change has brought a compelling need for cooling living spaces to the attention of researchers as well as construction professionals. The problem of overheating enclosures is now exacerbated in traditionally affected areas and is also affecting countries that were previously less prone to the issue. In this paper, we address measurements of thermal comfort and cooling emission efficiency parameters for different devices: ceiling panels, underfloor cooling, fan-assisted radiators, and fan coil. These devices were tested in low and high cooling capacities of up to 40 W/m2 while also featuring heating dummies to imitate internal heat gains. Air temperatures were measured at different heights, allowing to evaluate the thermal stratification with high accuracy. Thermal comfort differences of the tested systems were quantified by measuring both air velocities and operative temperatures at points of occupancy. In summary, the best-performing cooling devices for the studied cooling applications were the ceiling panels and fan radiators, followed by underfloor cooling, with a limitation of stratification. Because of the strong jet, fan coil units did not achieve thermal comfort within the whole occupied zone. The results can be utilized in future studies for cooling emission efficiency and energy consumption analyses of the different cooling devices.

Medium and heavy-duty battery electric trucks (BETs) may play a key role in mitigating greenhouse gas (GHG) emissions from road freight transport. However, technological challenges such as limited range and cargo carrying capacity as well as the required charging time need to be efficiently addressed before the large-scale adoption of BETs. In this study, we apply a geospatial data analysis approach by using a battery electric vehicle potential (BEVPO) model with the datasets of road freight transport surveys for analyzing the potential of large-scale BET adoption in Finland and Switzerland for trucks with gross vehicle weight (GVW) of over 3.5 t. Our results show that trucks with payload capacities up to 30 t have the most potential for electrification by relying on the currently available battery and plug-in charging technology, with 93% (55% tkm) and 89% (84% tkm) trip coverage in Finland and Switzerland, respectively. Electric road systems (ERSs) would be essential for covering 51% trips (41% tkm) of heavy-duty trucks heavier than 30 t in Finland. Furthermore, range-extender technology could improve the trip electrification potential by 3–10 percentage points (4–12 percentage points of tkm).

A solar-powered combined cooling, heating, and power (CCHP) plant integrated with a water electrolysis unit is investigated in terms of energy, exergy, and exergo-economic (3E) assessments. A comprehensive parametric study and optimization is conducted following the thermodynamic and exergo-economic assessment of the proposed system to evaluate the key performance parameters of the system for efficiency and economic factors. This system employs a heliostat field and a receiver tower by taking advantage of thermal energy from the sun and produces a continuous energy supply with an integrated phase-change material (PCM) tank to store the heat. In addition, a supercritical CO2 Rankine cycle (RC), an ejector refrigeration cooling (ERC) system, and a PEM water electrolyzer are coupled to produce cooling, heating, power, and hydrogen. Thermodynamic analysis indicates that the system exergy efficiency and energy efficiency are improved to 33.50% and 40.61%, respectively, while the total cost rate is 2875.74 USD/h and the total product cost per exergy unit is 25.65 USD/GJ. Additionally, the system produces a net generated power, heating load, and cooling load of 11.70, 13.92, and 2.60 MW, respectively, and a hydrogen production rate of 12.95 g/s. A two-objective optimization approach utilizing a non-dominated sorting genetic algorithm (NSGA) was performed, demonstrating that the system’s ideal design point offers a cost rate of 1263.35 USD/h and an exergetic efficiency of 34.17%.

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

Primary frequency reserves in Northern Europe have traditionally been provided with hydro plants and fossil fuel-burning spinning reserves. Recently, smart distributed energy resources have been equipped with functionality needed to participate on frequency reserves. Key categories of such resources include photovoltaic systems, batteries, and smart loads. Most of these resources are small and cannot provide the minimum controllable power required to participate on frequency reserves. Thus, virtual power plants have been used to aggregate the resources and trade them on the frequency reserves markets. The information technology aspects of virtual power plants are proprietary and many of the details have not been made public. The first contribution of this article is to propose a generic data model and application programming interface for a virtual power plant with the above-mentioned capabilities. The second contribution is to use the application programming interface to cope with the unpredictability of the frequency reserve capacity that the photovoltaic systems and other distributed energy resources are able to provide to the frequency reserves markets in the upcoming bidding period. The contributions are demonstrated with an operational virtual power plant installation at a Northern European shopping center, aggregating photovoltaic Primary Frequency Reserves resources.