A multi-energy system (MES) provides greater flexibility for the operation of different energy carriers. It increases the reliability and efficiency of the networks in the presence of renewable energy sources (RESs). Various energy carriers such as power, gas, and heat can be interconnected by energy storage systems (ESSs) and combined heat and power units at different levels (e.g., within a region or a local).Non-coordinated optimization of energy systems at local and regional levels does not verify the whole optimal operation of systems since the systems operate without considering their interactions with each other. One of the most famous sources of flexibility is ESSs. Hence, this paper presents a stochastic decentralized approach to evaluate the impact of ESSs on regional-local MES market-clearing within a bi-level framework. On the regional level, the economic interaction between the electricity and NG systems is carried out by a centralized system operator (CSO). In addition, coordination between various energy carriers is implemented by the energy hub operator at the local level. To ameliorate the flexibility of the natural gas (NG) system in the regional MES, the linepack model of gas pipelines has been considered.
Peroxisomes play an essential role in lipid metabolism via interaction with other intracellular organelles. The information about the role of the Acyl‐CoA‐binding domain containing‐protein 5 (ACBD5) in these interactions in human cells is emerging. Moreover, a few patients with retinal dystrophy and leukodystrophy caused by pathogenic variants in ACBD5 have been recently introduced. Here, we present a 36‐year‐old female with retinal dystrophy, leukodystrophy, and psychomotor regression due to a novel homozygous variant in ACBD5. Our study adds to the growing knowledge of this peroxisomal disorder by providing phenotypic details of the first adult patient.
The wind integrated multi-energy systems (MES) have gained significant momentum in recent years on account of their self-sufficiency and attractive clean attributes. This study puts forward a bi-level multi-follower optimization framework to study the tactical response of a wind integrated MES in the wholesale electricity market (WEM) and the natural gas market (NGM) as a price setter. At the upper level, the MES endeavors to minimize the overall operational costs by giving the best offer/bid in WEM/NGM, and by utilizing thermal energy storage (TES), compressed air energy storage (CAES), and natural gas storage (NGS). When the MES submits offers/bids in WEM and NGM, the NGM and WEM operators, as individual followers, clear their respective markets to maximize public welfare and announce the ultimate market-clearing price (MCP). Additionally, risk-averse and riskseeker information gap decision theory (IGDT) have been deployed to provide various decision-making options for MES operators considering wind underproduction and overproduction scenarios. Standard 6-node natural gas network (NGN) and 6-bus transmission system (TS) have been deployed to model WEM and NGM, respectively. The results testify to the capabilities of the MES in influencing the decisions of WEM and NGM.
The combined heat and power (CHP) plant is one of the emerging technologies of gas‐fired units, which plays an important role in reducing environmental pollutants and delivering high energy efficiency. Moreover, the hydrogen energy storage (HES) system with extra power storage from wind turbine via power to hydrogen technology allows the injection of stored energy into the power grid by reverse hydrogen to power services, offsetting in this way the uncertainty of wind power. Consequently, simultaneous usage of CHP and HES units not only makes the maximum use of wind power distribution but also increases flexibility and reduces the operating costs of the entire network. Therefore, this paper proposes an interval optimization technique for managing the uncertainty of wind power generation in the integrated electricity and natural gas (NG) networks considering CHP–HES. Moreover, to enhance the flexibility of the NG network, a linearized Taylor series‐based model is proposed for modelling linepack of gas pipelines in the proposed scheduling framework that is formulated mixed‐integer linear programming and solved using the Cplex solver. The obtained results indicate that the simultaneous use of CHP–HES in the day‐ahead scheduling reduces the operating cost and increases the flexibility of the whole network.
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