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This paper investigates the use of deep multi-agent reinforcement learning (MARL) for the coordination of residential energy flexibility. Particularly, we focus on achieving cooperation between homes in a way that is fully privacy-preserving, scalable, and that allows for the management of distribution network voltage constraints. Previous work demonstrated that MARL-based distributed control can be achieved with no sharing of personal data required during execution. However, previous cooperative MARL-based approaches impose an ever greater training computational burden as the size of the system increases, limiting scalability. Moreover, they do not manage their impact on distribution network constraints. We therefore adopt a deep multi-agent actor-critic method that uses a centralised but factored critic to rehearse coordination ahead of execution, such that homes can successfully cooperate at scale, with only first-order growth in computational time as the system size increases. Training times are thus 34 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes. Moreover, experiments show that the cooperation of agents allows for a decrease of 47.2% in the likelihood of under-voltages. The results indicate that there is significant potential value for management of energy user bills, battery depreciation, and distribution network voltage management, with minimal information and communication infrastructure requirements, no interference with daily activities, and no sharing of personal data. Applied Energy, 377, Part A ISSN:0306-2619 ISSN:1872-9118

In the light of global sustainability efforts, heat pumps offer environmental benefits, but their complexity and potential misconfigurations often lead to homeowner dissatisfaction due to inaccurate heating and lower-than-expected efficiency. Among the most important and complex settings is the heating curve and yet there are no easy-to-use methods to optimize it after its initial set-up. This study aims to develop ready-to-use guidelines for optimizing the heating curve with energy-efficient adjustments that improve room comfort and prevent suboptimal user changes, all without requiring additional sensors like room thermostats. Based on interpretable linear models, estimated on 3995 air-to-water heat pumps, located in Central Europe, we select the least energy-intensive heating curve shift for each outdoor temperature, needed to meet room thermal comfort. We find that the standard parallel shift of the heating curve is only the optimal approach when the average outdoor temperature is between 2 ⁰C and 5 ⁰C. Outside this range, the heating curve should be moved at its starting or the endpoint. Simulation shows that by translating user input to the room controller with our proposed changes, 84.42 % of the heating curves can be improved, reducing the share of misconfigured heating curves from 24.01 % to 7.08 %. This leads to an average reduction in yearly energy consumption of 4.02 % and an increase in the seasonal coefficient of performance by 2.59 % on average. By introducing ready-to-use heating curve improvement guidelines, we aim to increase efficiency and confidence in heat pump technology, ensuring its adoption to meet carbon emission targets. Applied Energy, 389 ISSN:0306-2619 ISSN:1872-9118

Carbon neutrality targets are laudable goals among all and sundry. However, the pathways to these goals remain a subject for broad discussion in the environmental debates. We explore how a more sustainable environment can be achieved via green finance, green innovative technology, and environmental fiscal regulations. Green finance was discussed as a public-private investment for energy. The empirical exploration essentially takes the lead initiative by integrating the moderating paths of structural changes and institutional quality (IQ) in the top emerging seven economies (E7). These economies notably contribute over 45 % of global carbon emissions. Thus, a robust analysis of the bloc's panel data (2000−2022) via the Augmented Mean Group (AMG), Driscoll-Kraay, and the Quantile-on-Quantile Regression techniques provides insightful information on the research focus. Firstly, we observed that green finance and environmental taxes significantly reduce carbon emissions in these top-emitting economies. Secondly, environmental regulation with(out) carbon taxes showed divergent impacts. Regulations without carbon tax in the model show a significant positive effect on emissions contrary to when the carbon tax was included. Thirdly, the duo of structural changes and IQ dampens the bloc's emissions. The specific pathways to achieve 15 %, 30 %, and 50 % reductions in environmental pollution using statistical model's coefficients suggest that financial strategies should be customized to each country's needs, with China and Russia benefiting from substantial green finance initiatives, while countries like India may focus on optimizing existing resources. Overall, the research suggests that the bloc can achieve SDG-7 of a quest for global access to sustainable energy and SDG-13 of climate action by leveraging innovative technology adoption and green funding measures vis-à-vis robust carbon tax regulations and implementations.

Decentralized multi-energy systems (MESs) are a key element of a future low-carbon energy supply. Here, we address the crucial role of grid-connected and off-grid MESs in achieving a low-carbon future, particularly relevant for regions like the Mediterranean with high renewable energy potential and carbon-intensive grid networks, using a mixed integer linear program for optimal economic and environmental MES design considering location-specific regulations. The results reveal that substantial cost (up to 30%, potentially saving 1.6 million) and greenhouse gas (GHG) emission reductions can be reached in Mediterranean regions with sufficient solar and wind energy resources currently relying on fossil fuel-based generators. However, our case study shows that the actual cost and emission reductions are most likely limited due to location-specific regulations (limiting cost savings to 0.8 million), especially those that constrain solar photovoltaic and onshore wind. Off-grid energy systems might be suitable decarbonization options in Mediterranean regions, to avoid absorbing current GHG-intensive power from the local power grid, under marginal cost increase (15%) compared to grid-connected cost optimization. However, off-grid MESs require substantial upfront investments and exhibit some environmental trade-offs, especially on material utilization, which could be overcome by balanced autonomy. Overall, truly sustainable and secure decentralized energy systems can only be reached by considering life cycle environmental impacts, social acceptance, and regulations during the design phase. Applied Energy, 377 ISSN:0306-2619 ISSN:1872-9118

The energy transition to local multi-energy systems has the potential to reduce the cost and carbon emissions of energy supply while increasing its reliability. However, cost, emissions, and security of energy supply have trade-offs that affect decision-making. This paper presents a multi-objective optimization model that quantifies the trade-offs among expected annual costs, life-cycle greenhouse gas emissions, and energy not served under single-failure events of local multi-energy systems. The developed model optimally sizes candidate conversion and storage technologies and operates them under nominal and failure modes. Results show that system planners can leverage the non-linearity in trade-offs for the best compromise among costs, emissions, and security of energy supply. Results for a Swiss neighborhood show that local multi-energy systems simultaneously reduce costs by 15%, emissions by 64%, and increase reliability by 80% over the business-as-usual way of meeting household electricity and heat demand. This best compromise optimal multi-energy system diversifies the heating supply by primarily utilizing heat pumps, supplemented by gas boilers and thermal storage, that mitigate grid supply interruptions and heat pump failures. While such heating systems reduce emissions significantly (64%), independence from gas-based heating will require heat pumps with reduced life-cycle impacts and high availability. In the electricity sector, the optimal choice of technologies to meet reliability targets differs for varying emission targets, i.e., CHP is favored for loose emission targets. In contrast, solar PV and batteries are favored for strict emission targets. Critically, maximizing reliability via multi-energy systems has a significant environmental impact in low-emission grids. Applied Energy, 383 ISSN:0306-2619 ISSN:1872-9118

The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country's scope, analyzing international interactions. In this study, we examine three Swiss energy policies, analyzing their impacts on both the national energy system and the cross-border electricity flows. We use a model inter-comparison approach with four electricity system models to explore scenarios involving Swiss renewable generation targets, the Swiss market integration, and the Swiss winter import limitations, in the context of various European electricity developments. The results indicate that a renewable generation target leads to a reduction in net imports and electricity prices. Additionally, reduced market integration impacts both Swiss and European energy transitions by limiting trade benefits, underutilizing Variable Renewable Energy Sources (VRES), and increasing electricity supply costs. Lastly, we observe that limiting Swiss winter imports adversely affects electricity trading, driving up both supply costs and electricity prices. The main body of the paper has 16 pages, 11 figures and 4 tables. The supplementary material has 7 pages, 6 figures and no tables