The escalating emissions of greenhouse gases have emerged as the primary driver of global warming. Cities have been found responsible for 70 % of global CO2 emissions. This brings high potential for lowering greenhouse gas emission by introducing innovative technologies in urban environments. One promising technology is Photovoltaics (PV) coupled with energy storage systems (ESS). In this paper, the deployment, integration, and operation of ESS coupled PV is being investigated. This involves installing a real-life PV-ESS system in the KTH Live-In-Lab. This paper focuses on the optimization of battery operations and prepares the framework for an automated operation of the real-life battery installed in the KTH Live-in-Lab. The focus centers on achieving two separate objective functions: minimizing costs and maximizing self-consumption. These goals are pursued through the application of linear optimization, day-ahead forecasting, and real-time control. The results compare the performance of change in solar power self-consumption and cost for each year. Where the ‘Base case’ scenario served as a benchmark with a yearly cost of 210,078 SEK in 2022 and self-consumption of 71.86 %. The cost optimization considers two different scenarios where monthly peak demand billing is considered or disregarded. Without considering the monthly peak power billing, costs drop by 32.2 % and self-consumption increases significantly to 96 %. Considering power charges reduce costs by 14 % and self-consumption was slightly improved to 98 % compared to the base-case. The optimization for maximizing self-consumption shows an improvement in costs of 3 % and achieves 100 % self-consumption. The forecasting-optimization framework introduced in this study is a valuable decision support tool, aiding stakeholders in making informed choices about balancing costs and self-consumption for PV systems integrated with battery energy storage systems in grid-connected apartment buildings. The tool is adaptable and can be trained for different sites and is capable of handling different cost structures to evaluate the full energy trading landscape. The findings show that the battery dispatch strategies must be dynamic regardless of the season. This is due to the interplay between energy availability, market prices, and grid interactions to optimize performance and cost. They also highlight that for larger grid connections (above 80 A) a more flexible power billing structure would improve the financial viability of PV-BESS systems for the end user.

The growing adoption of electric vehicles (EVs) presents both opportunities and challenges for electricity systems, particularly in balancing supply and demand through ancillary service markets. In Sweden, plug-in EVs accounted for over 58% of new car registrations in 2024, making grid stability increasingly important. This research investigates the potential of leveraging residential EV flexibility to participate in the Manual Frequency Restoration Reserve (mFRR) market. Using real-world charging data from a sample of 3127 EV chargeboxes from Greenely AB and market data from the Swedish transmission system operator, Svenska kraftnät (SvK), a machine learning (ML) framework is developed to forecast EV availability, defined as the aggregated minimum hourly charging demand. The study compares Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and Seasonal ARIMA (SARIMA) models, with XGBoost achieving the highest accuracy. These forecasts feed into a Linear Programming Optimization model designed to maximize household revenue by shifting charging to periods with favorable mFRR capacity and mFRR prices, while meeting market, technical, and regulatory constraints. Results show that bi-directional smart charging significantly improves the economic feasibility of mFRR participation, with the most effective scenario increasing net revenue by 45.5% over the baseline. The study also identifies feasible bidding hours, regulatory limitations, and strategies for electricity aggregators, forecasting up to €140,061 in annual gross earnings. By combining ML-based forecasting with the optimization model, this research addresses a key gap in the literature and offers practical insight for EV usage, market bidding strategies, and energy aggregator business models.

Global floor area is increasing every year which is subsequently leading to an increase in electricity and heating demand in buildings. Residential buildings have huge potential for energy savings and there is an immediate need to decarbonize them by the end of 2050. Machine learning is finding application across all fields and will thus have an important role to play in the building sector also. One of the important challenges that building owners need to tackle is occupancy detection in residential apartments which can help save considerable amounts of energy and costs. However, occupancy is highly variable, and it is difficult to quantify and predict occupancy because of the random and individualistic nature of humans. In addition, scalable approaches for occupancy detection should prioritize data from common and cost-effective sensors like temperature sensors. In contrast to existing literature which has stated that occupancy detection based on the data from a single environmental sensor is not appropriate for obtaining good results, this paper aims to detect occupancy in a real residential building using only indoor temperature as the feature to train the model. Different machine learning models and techniques are studied and tested to understand how the accuracy of occupancy detection can be increased. With the right techniques, it has been possible to obtain promising results in the form of an accuracy of 95% using machine learning models and only indoor temperature to train it.

Demand charges and Time-of-use pricing are fundamental elements of contemporary electricity markets, introducing complexities in the operation of microgrids. Time-of-use pricing incentivizes energy consumption during off-peak hours, while demand charges impose fees based on peak power usage, significantly impacting electricity costs for both residential and commercial users. This research investigates the potential of battery energy storage systems to mitigate these costs by reducing demand charges and facilitating energy arbitrage. A significant challenge in determining optimal battery size lies in the uncertainties associated with building load predictions. Therefore, the study addresses critical uncertainties in load forecasts driven by climate change and occupant behavior. A novel stochastic framework is proposed that integrates these uncertainties into building load forecasts and considers demand charges in the optimization process. By employing the K-medoids clustering method in conjunction with the Bayesian information criterion, the framework achieves a remarkable reduction in computation time of 75.4% to 87.4%, while preserving essential load variability. The stochastic framework results in an overall cost reduction of 5.7%, alongside a 13.3% increase in the optimal battery size. Furthermore, implementing the proposed framework leads to a peak demand reduction of up to 25.8%.

This study investigates the techno-economic and techno-environmental performance of photovoltaic (PV) solar systems coupled with battery energy storage systems (BESS) in a Swedish context. The research uses mixed-integer linear programming (MILP) to optimise the dispatch strategy, minimising both operational costs and CO2eq emissions. By analysing grid signals, including electricity price and carbon intensity, the study determines the optimal size and operation of PV-BESS. The base case in Sweden was further examined by comparing the system with Italy and Poland, and by testing it with different load demand profiles. Italy and Poland were chosen due to their higher variability in grid price and carbon footprint, respectively, as well as more favourable solar conditions. The industrial and industrial shift profiles were chosen to assess the impact of load profiles with less variability compared to the base case residential profile. The key findings reveal distinct differences between economic and environmental optimisation, impacting system performance and highlighting the need for a balanced approach. Local conditions, such as grid signal volatility and solar PV production, are shown to significantly influence optimal system configurations. In Sweden, the economic approach led to higher system utilisation due to greater price volatility, while the environmental approach prioritised lower emissions. Additionally, the trade-offs between economic and environmental optimisation can lead to cost/environmental footprint increases between 25% and up to several times higher (up to 300 %). The study also finds that reducing the levelised cost of energy (LCOE) or levelised carbon footprint (LCO2eq) from the investor perspective may not always translate into significant end-user benefits. This further highlights the importance of including various stakeholder perspectives in the analysis, especially in the context of decision support. Sensitivity analysis indicates that oversizing the PV system leads to a rapid increase in costs and emissions. The addition of BESS can justify this increase by scaling the Renewable Energy Self-Sufficiency (RESS) value. Furthermore, there are diminishing returns for oversizing the battery. This research is relevant for various stakeholders, including project developers, policymakers, and researchers involved in renewable energy integration. Future research could further refine optimisation strategies for PV-BESS systems by delving deeper into specific aspects such as grid signal analysis and diverse end-of-life (EoL) pathways.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.

Oil-dependent countries face persistent challenges, such as energy supply-demand imbalances, overreliance on fossil fuels, declining economic diversification, and environmental degradation. In response, policymakers are increasingly advocating for comprehensive energy transitions to enhance energy and environmental security while promoting sustainable development. This study evaluates Iran's energy transition through the modeling of five scenarios using the EnergyPLAN software V16.3. These scenarios, ranging from increased fossil fuel production to renewable energy deployment, subsidy reform, and energy efficiency, were developed based on a systematic literature review and expert interviews. Key indicators such as carbon emissions, primary energy demand, and supply-demand balance were used to assess the long-term impacts of each scenario through 2040. The Transition Scenario Policy (TSP), which integrates elements of all other scenarios, emerged as the most effective pathway for reducing emissions, correcting supply-demand imbalances, and aligning with sustainable development goals. The novelty of this study lies in its mixed-method approach, combining qualitative stakeholder insights with quantitative modeling, offering a replicable framework for energy transition planning in similar oil-dependent contexts. The practical implications support evidence-based policy making, while the results open avenues for future research on adaptive energy governance, policy trade-offs, and resilience under global uncertainty.

Carbon emissions significantly contribute to global warming, amplifying the occurrence of extreme weather events and negatively impacting the overall environmental transformation. In line with the global commitment to combat climate change through the Paris Agreement (COP21), the European Union (EU) has formulated strategies aimed at achieving climate neutrality by 2050. To achieve this goal, EU member states focus on developing long-term national strategies (NLTSs) and implementing local plans to reduce greenhouse gas (GHG) emissions in alignment with EU objectives. This study focuses on the case of Sweden and aims to introduce a comprehensive data-driven framework that predicts CO2 emissions by using a diverse range of input features. Considering the scarcity of data points, we present a refined variation of multi-task learning (MTL) called weighted multi-task learning (WMTL). The findings demonstrate the superior performance of the WMTL model in terms of accuracy, robustness, and computation cost of training compared to both the basic model and MTL model. The WMTL model achieved an average mean squared error (MSE) of 0.12 across folds, thus outperforming the MTL model’s 0.15 MSE and the basic model’s 0.21 MSE. Furthermore, the computational cost of training the new model is only 20% of the cost required by the other two models. The findings from the interpretation of the WMTL model indicate that it is a promising tool for developing data-driven decision-support tools to identify strategic actions with substantial impacts on the mitigation of CO2 emissions.

Uncontrolled open dumping and burning of municipality solid waste (MSW) has resulted in soil, water, and air pollution in many urban cities in India. Landfills are the most common cost-effective solution for MSW management in many developing countries like India. However, the identification of suitable landfill sites always remains a challenging task as it involves selection of several environmental criteria set by the local authorities. The objective of this study is to identify the most potential landfill sites proposed by the Government in Bengaluru Metropolitan Region, Karnataka state, India using Geographic Information System enabled Analytical Hierarchy Process based multi-criteria evaluation technique. Several criteria and constraints as recommended by the local authorities along with the proximity to the solid waste processing plants are used to identify the potential landfill sites in the study region. The study identified three highly suitable sites (Neraluru, Gudhatti, Madivala) for landfills which are not only environmentally sustainable but also economically attractive as they are closer to the solid waste processing plants minimizing the transportation cost involved in the disposal of solid waste from the source to the final disposal sites in the study region.