Nowadays, problems facing Distribution System Operators (DSOs) due to demand increase and the wide penetration of renewable energy are usually solved by means of grid reinforcement. However, the ...smart grid paradigm enables the deployment of demand flexibility for congestion management in distribution grids. This could substitute, or at least postpone, these needed investments. A key role in this scheme is the aggregator, who can act as a “flexibility provider” collecting the available flexibility from the consumers. Under this paradigm, this paper proposes a flexibility market led by the DSO and aimed at solving distribution grid congestions. The proposal also includes a flexibility market clearing algorithm, which is easy to implement, has low computational requirements and considers the energy rebound effect. The proposed design has the advantage of excluding the DSO’s need for trading in energy markets. Also, the solution algorithm proposed is fully compatible with already existing grid analysis tools. The proposed electricity market is tested with two case studies from a real Spanish distribution network, where the proposed clearing algorithm is used, and finally, results are presented and discussed.
The role of the distribution system operator (DSO) is evolving with the increasing possibilities of demand management and flexibility. Rather than implementing conventional approaches to mitigate ...network congestions, such as upgrading existing assets, demand flexibility services have been gaining much attention lately as a solution to defer the need for network reinforcements. In this paper, a framework for a decentralized local market that enables flexibility services trading at the distribution level is introduced. This market operates on two timeframes, day-ahead and real-time and it allows the DSO to procure flexibility services which can help in its congestion management process. The contribution of this work lies in considering the uncertainty of demand during the day-ahead period. As a result, we introduce a probabilistic process that supports the DSO in assessing the true need of obtaining flexibility services based on the probability of congestion occurrence in the following day of operation. Besides being able to procure firm flexibility for high probable congestions, a new option is introduced, called the right-to-use option, which enables the DSO to reserve a specific amount of flexibility, to be called upon later if necessary, for congestions that have medium probabilities of taking place. In addition, a real-time market for flexibility trading is presented, which allows the DSO to procure flexibility services for unforeseen congestions with short notice. Also, the effect of the penetration level of flexibility on the DSO’s total cost is discussed and assessed. Finally, a case study is carried out for a real distribution network feeder in Spain to illustrate the impact of the proposed flexibility framework on the DSO’s congestion management process.
Peer-to-Peer (P2P) energy trading, which allows energy consumers/producers to directly trade with each other, is one of the new paradigms driven by the decarbonization, decentralization, and ...digitalization of the energy supply chain. Additionally, the rise of blockchain technology suggests unprecedented socio-economic benefits for energy systems, especially when coupled with P2P energy trading. Despite such future prospects in energy systems, three key challenges might hinder the full integration of P2P energy trading and blockchain. First, it is quite complicated to design a decentralized P2P market that keeps a fair balance between economic efficiency and information privacy. Secondly, with the proliferation of storage devices, new P2P market designs are needed to account for their inter-temporal dependencies. Thirdly, a practical implementation of blockchain technology for P2P trading is required, which can facilitate efficient trading in a secured and fraud-resilient way, while eliminating any intermediaries’ costs. In this paper, we develop a new decentralized P2P energy trading platform to address all the aforementioned challenges. Our platform consists of two key layers: market and blockchain. The market layer features a parallel and short-term pool-structured auction and is cleared using a novel decentralized Ant-Colony Optimization method. This market arrangement guarantees a near-optimally efficient market solution, preserves players’ privacy, and allows inter-temporal market products trading. The blockchain layer offers a high level of automation, security, and fast real-time settlements through smart contract implementation. Finally, using real-world data, we simulate the functionality of the platform regarding energy trading, market clearing, smart contract operations, and blockchain-based settlements.
•Decentralized P2P energy trading platform composed of market and blockchain layers.•Pool-structured market layer offering inter-temporal market products.•Novel decentralized market clearing method based on ACO.•Smart contract integrated in blockchain layer for secure real-time settlements.•Energy trading platform simulation based on real network data.
The heat sector accounts for almost half of the world’s energy consumption, making it a crucial component in meeting decarbonization targets. One of the biggest challenges of heat energy ...decarbonization arises at the household level, which collectively has a substantial impact on decarbonization. Individual households commonly rely on decentralized heat sources (DHS). Different technologies can be used as a DHS, each of which leads to different overall (Capex and Opex) costs and carbon emissions. Households choose their DHS technologies freely, though typically influenced by the recommendations from their local city planners who use historical data and analyze the respective economical and environmental consequences. Therefore, developing a decision support system (DSS) that guides individuals in their DHS investment choices is a high priority for city planners as it can help evaluate the role of different policies in aligning economical and environmental concerns. This is becoming more important as both the economical and environmental concerns are increasing, respectively, due to recent energy price spikes and the growing urgency of decarbonization. However, developing such a comprehensive DSS is challenging due to the complexity of accounting for all DHS constraints and the uncertainties in demand, prices, and policies. In this study, we present a reliable and comprehensive DSS that provides a range of optimal strategies including the most cost-efficient and the most environmentally friendly ones. These strategies identify the optimal type, installed capacity, and year of investment of DHS technologies, as well as the expected yearly heat generation of each technology. Our DSS accounts for the uncertainties of heat demand, fuel prices, investment, and operation and maintenance costs. We apply our DSS to a typical household in the municipality of Lyngby-Taarbæk under different policy scenarios. We show that between the most environmentally friendly and most cost-efficient solution only a gap of 9%–15% in cost needs to be bridged. We also demonstrate that current energy taxation policies in Denmark do not provide a level playing field between different heat technologies. Under different policy scenarios, we show that heat pumps integrated with PV have the highest potential for minimizing CO2 emissions for a Danish household.
•We develop a reliable DSS for optimal long-term individual heat technology choices.•Our model can show the impact of policies on optimal individual heat technologies.•The carbon-neutral solution is 9%–15% more costly than the most cost-efficient one.•Heat pumps with PV are the most promising heat technology solution for households.•Current energy taxation policies are not fair across different heat technologies.
A novel imidazolium-based cationic Gemini surfactant (GS) was synthesized, characterized and evaluated as a corrosion inhibitor for carbon steel in 0.5 M H2SO4 using electrochemical impedance ...spectroscopy (EIS) and potentiodynamic polarization techniques. The results revealed that this Gemini surfactant can act as an effective mixed-type inhibitor (ηp ∼ 90%) for carbon steel via adsorption of its molecules on the metal surface according to Langmuir adsorption isotherm. The synthesized GS showed a good synergistic inhibitory action with Ce3+cations and Iˉ anions in 0.5 M H2SO4 medium (ηp ∼ 93% and 95%, respectively). Thermodynamic activation parameters for the dissolution process of carbon steel in blank and inhibited sulfuric acid solution were calculated and discussed. A suitable equivalent circuit was used to analyze the experimental EIS data and modeling the inhibition process. Quantum chemical calculations have revealed that the tested Gemini surfactant can interact with Fe surface through electron transfer from the metal atom to surfactant molecule.
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•A novel Gemini surfactant was synthesized and its structure was confirmed.•Its anticorrosion effect was investigated using various electrochemical techniques.•Calculated quantum chemical parameters corroborate with experimental results.
Variable Renewable Energy (VRE) sources are characterized by production uncertainty that is largely due to weather forecast errors. This leads to greater volumes in the real-time balancing markets ...that drive Imbalance Market (IM) price higher, creating economic opportunities for flexibility providers. However, lack of information on foreseen IM prices and regulation states at the time of day-ahead market closure, reduces the flexibility providers' opportunity of optimizing their portfolio. The objective of this paper is to investigate to which extent there is a correlation between the IM prices and weather parameters in the Dutch Imbalance Market. A Deep Learning (DL) model based on Feed-Forward Deep Neural Network (FFDNN) is developed with the aim to support VRE asset owners and flexibility providers to predict IM price ranges and regulation states in the day-ahead horizon. The parameters considered are temperature, solar radiation, wind speed, relative humidity, and cloud cover. A benchmark model using Support Vector Machine (SVM) is used to compare with the DL's model performance. Both models are trained and tested using data from the weather prediction model provided by Royal Netherlands Meteorological Institute (KNMI), and historical IM prices from the Dutch Transmission System Operator (Tenne'T), for the years 2018-2020.
In the past decades, Distribution System Operators (DSOs) have been mitigating distribution networks (DNs) contingencies by opting to grid reinforcements. However, this approach is not always cost ...and time efficient. Demand Side Flexibility (DSF) is one of the recent alternatives used in DNs congestion management. Consequently, new market players such as aggregators are needed to handle DSF transaction between customers and DSOs. This paper proposes and models a decision support program (DSP) to optimize the total cost charged by the DSO for using DSF services. Moreover, the energy rebound effect is taken into consideration as well as the uncertain behavior of customers. Finally, the distribution grid of the Danish Bornholm Island is used to illustrate the merits of the DSP. The total cost incurred by the DSO is calculated and presented.
Micro-grids have spread in many distribution systems worldwide. They offer safe and reliable operation for their consumers by managing the micro-grids' power generation and trading with the main ...grid. Furthermore, micro-grids can help in integrating and promoting for Renewable Energy Sources (RES) and reducing the environmental impacts of traditional centralized generation. This paper proposes multiple environment dispatch problem solution in micro-grids using Ant Colony Optimization (ACO) technique to solve the generation dispatch problem. A combined cost optimization scheme is investigated to minimize both operational cost and emission levels while satisfying the micro-grid's load demand. Furthermore, the proposed multiple environment dispatch problem solution is used to evaluate promoting RES implementation in micro-grids despite of their high capital cost using the combined economic emission dispatch problem. The proposed multiple environment dispatch solution was implemented using MatLab and tested on two case studies with and without RES-Wind Turbine-Solar (WT-PV). The obtained results from the proposed technique are compared with those calculated using other Techniques; Gradient to evaluate the proposed method. The outcomes are evaluated and discussed. Finally, conclusions are reported.
AIM: To correlate a genetic polymorphism of the low-density lipoprotein(LDL) receptor with antiviral responses in Egyptian chronic hepatitis C virus(HCV) patients.METHODS: Our study included 657 ...HCV-infected patients with genotype 4 who received interferonbased combination therapy. Patients were divided into two groups based on their response to therapy: 356 were responders, and 301 were non-responders. Patients were compared to 160 healthy controls. All patients and controls underwent a thorough physical examination, measurement of body mass index(BMI) and the following laboratory tests: serum alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, albumin, total bilirubin, direct bilirubin, prothrombin time, prothrombin concentration, INR, complete blood count, serum creatinine, fasting blood sugar, HCV antibody, and hepatitis B surface antigen. All HCV patients were further subjected to the following laboratory tests: HCV-RNA using quantitative polymerase chain reaction(PCR), antinuclear antibodies, thyroid-stimulating hormone, an LDL receptor(LDLR) genotype study of LDLR exon8 c.1171G>A and exon-10 c.1413G>A using real-time PCR-based assays, abdominal ultrasonography, ultrasonographic-guided liver biopsy, and histopathological examination of liver biopsies. Correlations of LDL receptor polymorphisms with HAI, METAVIR score, presence of steatosis, and BMI were performed in all cases.RESULTS: There were no statistically significant differences in response rates between the different types of interferon used or LDLR exon10 c.1413G>A. However, there was a significant difference in the frequency of the LDL receptor exon8 c.1171G>A genotype between cases(AA: 25.9%, GA: 22.2%, GG: 51.9%) and controls(AA: 3.8%, GA: 53.1% and GG: 43.1%)(P < 0.001). There was a statistically significant difference in the frequency of the LDLR exon 8C:1171 G>A polymorphism between responders(AA: 3.6%, GA: 15.2%, GG: 81.2%) and nonresponders(AA: 52.2%, GA: 30.6%, GG: 17.2%)(P < 0.001). The G allele of LDL receptor exon8 c.1171G>A predominated in cases and controls over the A allele, and a statistically significant association with response to interferon was observed. The frequency of the LDLR exon8 c.1171G>A allele in non-responders was: A: 67.4% and G: 32.6 vs A: 11.2% and G: 88.8% in responders(P < 0.001). Therefore, carriers of the A allele exhibited a 16.4 times greater risk for nonresponse. There was a significant association between LDL receptors exon8 c.1171G>A and HAI(P < 0.011). There was a significant association between LDL receptors exon8 c.1171G>A and BMI. The mean BMI level was highest in patients carrying the AA genotype(28.7 ± 4.7 kg/m2) followed by the GA genotype(28.1 ± 4.8 kg/m2). The lowest BMI was the GG genotype(26.6 ± 4.3 kg/m2)(P < 0.001). The only significant associations were found between LDL receptors exon8 c.1171G>A and METAVIR score or steatosis(P < 0.001).CONCLUSION: LDL receptor gene polymorphisms play a role in the treatment response of HCV and the modulation of disease progression in Egyptiansinfected with chronic HCV.
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