2023

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    Energy storage strategy analysis based on the Choquet multi-criteria preference aggregation model: The Portuguese case
    (Socio-Economic Planning Sciences, 2023) Pereira, A, ; Pereira, M.
    With the increase in renewable energy generation and its problems related to output instability, storage systems must be implemented in parallel to account for this effect. Therefore, it is valuable to deepen the study of these technologies’ performances in their several application tiers, thus understanding the potential of each alternative, both per tier and as a whole. For this reason, a collaborative multi-criteria decision-aiding framework is proposed to rank the various available options in several layers of the energy storage market, constructed alongside experts and policy-makers from each tier that serve as actors of the decision-making process and using Portugal as a case study. Based on the Choquet multi-criteria preference aggregation model, to the best of the authors’ knowledge, this framework is an unprecedented application in the energy sector. Beyond a critical review of the results, a scenario analysis was performed to explore interesting future possibilities that may aid governments to make decisions in the search for an energy sustainable development. Chemical storage solutions, such as Hydrogen and Methane, as well as several electrochemical batteries, especially Lithium- and Nickel-based ones, were the standout energy storage solutions. Chemical storage was shown to have the desired characteristics for the Long-term grid tier. Meanwhile, batteries, including Redox Flow in the first case, have overperformed in the Microgrid and Mobility tiers. No standout solutions appeared in the Short-term grid tier. Unsurprisingly, the aforementioned chemical storage systems, batteries, and Hot Water have presented themselves as the most politically interesting technologies, due to their multipurpose uses and intrinsic characteristics.
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    Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options
    (energies, 2023) Heymann, F. ; Milojevic, T. ; Covatariu, U. ; Verma, P.
    Digitalization is a megatrend that affects and transforms societal, economic, and environmental processes on a global scale. Driven by a combination of technological advances as well as shifting societal demands, digitalization also affects the operation and planning of the electricity sector. This paper uses megatrend analysis framework to analyze digitalization phenomena, its regional differences, technologies, use cases and challenges. It highlights potential system-level benefits (e.g., increased efficiency, transparency, consumer participation) and challenges (e.g., electricity demand growth, autonomy loss, increasing cyber risks) currently reported in the literature. Eventually, building on the thorough analysis, we present a menu of policy options to exploit its full potential of digitalized electricity systems while mitigating adverse effects on decarbonization goals and consumers.
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    Using Reinforcement Learning to Reduce Energy Consumption of Ultra-Dense Networks With 5G Use Cases Requirements
    (IEEE Access, 2023) Malta, S. ; Pinto, P. ; Fernandez-Veiga, M.
    In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.
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    Design of an Energy Policy for the Decabornisation of Residential and Service Buildings in Northern Portugal
    (energies, 2023-02-03) Capelo, S.; Soares, T.; Azevedo, I.; Fonseca, W.; Matos, M.
    The decarbonisation of the building sector is crucial for Portugal’s goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse Gas (GHG) emissions reduction. Among the alternatives assessed, the proposed one (Alternative 4) presents the best returns on investment in terms of cost savings and emissions reduction. It allows for 52% investment cost savings in the residential sector and 13% in the service sector when compared to the current national roadmap to carbon neutrality (Alternative 2). The estimated emission reduction in 2050 for Alternative 4 is 0.64% for the residential sector and 3.2% for the service sector when compared to Alternative 2.