1,720,971 research outputs found
Flexibility of multi-energy systems: the role of Power-to-Gas and Power-to-Heat at the district level
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Optimising energy flows and synergies between energy networks
Full text linkThe increased use of fluctuating renewable energy sources (RES) that is expected in the near future will
lead to challenges concerning their full integration in the distribution grid, the reduction of RES curtailments
and the mitigation of electric unbalances on the grid. Besides electric batteries (EB), other
technologies, such as Power-to-Gas (P2G), Power-to-Heat (P2H) and Combined Heat and Power (CHP),
make it possible to exploit synergies between various energy networks, thus alleviating problems of RES
integration. In fact, when these technologies work simultaneously in a single energy system, their
installed power mix, along with their optimised management and control, play a fundamental role in the
energy optimisation of the whole system.
The aim of this paper is to offer a methodological approach for the analysis of the synergies between
the different energy networks in order to cope with the increasing RES penetration. The proposed model
has been used to perform a sensitivity analysis on the installed capacity of the various technologies;
moreover, different simplified system management logics have been analysed by changing the priority
order of the renewable energy surplus usage. The obtained results have been compared from an energetic,
economic and environmental point of view
Multi-Agent Deep Reinforcement Learning for Optimized Operation of Industrial Energy Systems
No full textIndustrial energy systems often supply diverse energy vectors: electricity, steam, hot and chilled water. To meet these demands, they integrate various technologies, including cogeneration units (e.g., internal combustion engines or microturbines), steam generators, chillers, and renewable sources like photovoltaic arrays. These components differ in efficiency, flexibility, and operational constraints, creating a tightly coupled and complex optimization problem. Traditional rule-based control strategies, common in practice, often fail to handle the variability of renewables and the complexity of multi-energy infrastructures. In this context, Deep Reinforcement Learning (DRL) has emerged as a compelling alternative. In DRL, an agent learns optimal policies through trial and error with an environment: it is a flexible framework for control and can be implemented using different structural paradigms, depending on how decision-making is distributed. Typically, only a subset of technologies, those with greater flexibility and cost impact, are directly controlled, while others respond indirectly to upstream decisions. This modularity suits decentralized or hierarchical control, where decision-making is distributed across multiple DRL agents to improve scalability and responsiveness. This work presents different DRL structures (centralized and decentralized) for the optimization of an industrial multi-energy system. All DRL models were benchmarked against a typical rule-based controller and a MILP-based optimization. Results show DRL outperforms the rule-based strategy, which cannot account for renewable variability. Among DRL configurations, performance varied, highlighting the importance of control architecture. Notably, the best-performing DRL setup used a hierarchical configuration, achieving results close to the MILP optimum and demonstrating the potential of hierarchical DRL agents for efficient, scalable energy management
Deep reinforcement learning as a tool for the analysis and optimization of energy flows in multi-energy systems
Full text linkDeep Reinforcement Learning algorithms not only facilitate the development of optimized control strategies but also serve as powerful tools to explore complex problems and uncover non-obvious control solutions. This paper investigates the application of Deep Reinforcement Learning to optimize a Multi-Energy System in the presence of high Renewable Energy Source penetration. Key energy conversion technologies, such as Combined Heat and Power, Battery Energy Storage Systems, Heat Pumps, and Power-to-Gas, enable bidirectional energy exchanges across different networks, thereby fostering operational synergies. Since these interconnections create in terdependencies in which energy flows within one sector significantly affect those in another, the complexity of optimization increases. The aim of this study has been to demonstrate the benefits of a method that can be used to interpret strategies implemented by a Deep Reinforcement Learning algorithm, thereby ultimately increasing the possibility of making optimal decisions. This approach has led to the creation of an optimized rule-based mechanism which has been used to analyze the Multi-Energy System, identify the most advantageous technol ogies (heat pumps, electric batteries and power-togas, respectively), and highlight the importance of imple menting an optimized strategy to achieve effective energy management. Such an optimized strategy led to a reduction in natural gas consumption of about 15%, a decrease in CO2 emissions of 18%, and a reduction in fuel and electricity costs of 17%
Energy Assessment of a Slow Pyrolysis Plant for Biochar and Heat Cogeneration
Full text linkThe production of biochar by slow pyrolysis systems is a promising technology to achieve negative emissions. In this paper, an energy analysis model for a biomass-based biochar production plant utilising the slow pyrolysis process is presented. The model was used to evaluate the energy analysis of a plant processing lignocellulosic biomass. The energy analysis considered the main technical parameters of this type of plant, including the moisture content of the biomass, the moisture after drying, the pyrolysis temperature and the characteristics of the biomass. The results showed the heat losses for each component of the plant and the recoverable useful heat from the production process, which represents about 16% of the chemical energy originally contained in the biomass. Although this is a secondary output, this aspect is important for analysing the sustainability of the biochar production chain. The energy analysis enabled by the model presented is indeed a valuable tool for the technical, economic and environmental assessment of biochar production plants
Integration of power to gas and power to heat systems into the electricity grid by the mean of flexibility service for the aggregators
No full textElectricity network is facing growing push to increase Renewable Energy Sources’ (RES) share in total energy production while guaranteeing acceptable quality of service and reliability. The issue is more than just an option and it is in fact the matter of passed legislations and defined targets that leaves no space for reverse steps. The targets however come in conflict where grid de-carbonization and therefore RES share increase in turn bring relative uncertainties and thus electrical system reliability would be penalized. Such challenges have triggered wide range of research projects in favour of RES integration to the electricity grid while keeping reliability high.
In the present research work -the goal of the European H2020 Planet project- we study how the union of the mentioned –partially- conflicting targets, opens a promising market for conversion and storage systems, whose assets could come in service for addressing a vast range of problems in the future electricity grid. We will study orienting on non-preliminary form of energy conversion and storage systems like Power to Heat (P2H) and Power to Gas (P2G), and that how those can alleviate critical issues such as Reverse Power Flow, Power Quality and Over Loadings, while de- carbonizing.
Although P2G unit isn’t a very recent technology, but its relatively high cost of investment and maintenance, has restricted drastically its usage regardless of its immediate benefits. We analyse these as flexibility providers to the grid operator in the era of Smart Grid, and show how this strategy brings interesting opportunities, for a multi-products asset.
In the present paper, we review the technical feasibility and efficient strategies to integrate P2G and P2H systems into the electricity system as flexibility providers to the aggregators, as well as the decision-making support system and ICT service architecture to hosting such functionalities. Finally, we discuss the direct impact of the complex system on reducing RES curtailment, avoiding grid congestion and needs for reinforcement and CO2 emission
Analysis of different combined cycles and working fluids for LNG exergy recovery during regasification
No full textIt has been estimated that the world's consumption of Liquefied Natural Gas (LNG) will increase
significantly over the next 20 years, thus making exergy recovery from the regasification process a
fundamental issue. When LNG is regasified in order to distribute the fuel through a pipeline network, a
large amount of exergy is released.
Three combined cycle schemes for energy generation have been analysed in this paper: the first one is
a direct expansion cycle, combined with a Rankine cycle, the second one presents a double expansion
with reheating and a recovery heat exchanger, and the last one shows two parallel Rankine cycles
working under different turbine pressures. The performance of the three cycles has been compared, and
the effects of using working fluids with different characteristics have been analysed in detail. Twelve
working fluids were selected, according to their thermodynamic, ambient and safety proprieties. The
working pressure and temperature that maximise the specific work have been found for each cycle and
fluid
Power-to-Gas in gas and electricity distribution systems:A comparison of different modeling approaches
Full text linkPower-to-Gas (P2G) has been one of the most frequently discussed technologies in the last few years. Thanks to its high flexibility, it can offer services to power systems, thereby fostering Variable Renewable Energy Sources (VRES) and the electricity demand match, mitigating the issues related to VRES overproduction. The analysis of P2G systems used at the distribution level has only been dealt with in a few studies: however, at this level, critical operation conditions can easily arise, in both the electrical infrastructure and in the gas infrastructure. The choice of appropriate modeling approaches for a P2G plant, as well as for the electricity and gas distribution grids is necessary to avoid overestimating or underestimating the potential flexibility that P2G plants connected to distribution networks can offer. The study presents a methodological analysis on the impact of different simulation approaches when P2G is installed at a distribution system level. The aim of this paper has been to understand the impact of different modeling approaches in order to determine whether, and under what conditions, they could be adopted. An illustrative case study has been developed to perform this analysis. The results show that the flexibility of the P2G technology can also be used at the distribution level; nevertheless, a correct modeling approach is necessary to properly evaluate the potential of this solution. The placement of P2G systems within the electricity network can affect the performance of the plant to a great extent. Therefore, it is necessary to use a model that takes into account the topology and energy flows of the electrical network. It was found, in the analyzed case study, that the use of an inappropriate electricity network model can lead, depending on the conditions, to either an overestimation or an underestimation (of 50 % and 40 %, respectively) of the ability of P2G plants to absorb VRES over-generation. The accuracy of the gas network and of the P2G plant models also plays an important role. In conditions of low gas consumption, it is necessary to consider the gas flows and the line-pack potential of the gas network, as well as the interactions between the components of the P2G plant in order to avoid underestimating the flexibility of the entire system. In the analyzed case study, the use of a simplified model of the gas network led to an underestimation of the accumulation potential of the over-generations of VRES of about 30 %, while the use of a simplified model for the simulation of P2G plants led to a 10 % underestimation of the storage potential.</p
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