Residual Demand Research Articles

Model-based analysis to identify the impact of factors affecting electricity gaps during COVID-19: A case study in Germany

The recent COVID-19 pandemic has precipitated drastic changes in economic and lifestyle conditions, significantly altering residual electricity demand behavior. This alteration has expanded the demand gap between actual and forecasted electricity usage based on pre-pandemic data, highlighting a critical global issue. Many studies in the pandemic have explored the features of this widening gap, which is impacted by major social events like fast virus spread and lockdowns. However, the influence of factors like economic shifts and lifestyle changes on this demand remains largely unexplored, primarily due to the pandemic's significant effects in these areas. Understanding the essential factors affecting the demand gap is crucial for stakeholders in the electricity sector to develop effective strategies. This study examines the hourly electricity consumption and related factors during the specified period. We present a method combining time-series forecasting and sparse modeling. This helps identify critical factors affecting the electricity demand gap during the pandemic, highlighting the most crucial variables. Utilizing this method, we identify the variables that have undergone significant changes during the pandemic and evaluate their effects on the electricity demand gap. The effectiveness is proven by applying it to the dataset collected in German.

Development of a maintenance scheduling method for power generators considering large penetration of variable renewable energy sources and system inertia

AbstractConventional methods to schedule power generators maintenance aim to secure enough supply reserve margin to the power system when its power generators have to go under maintenance. However, the use of such methods in scenarios of large penetration of VRE, might be problematic. This is because in such scenarios conventional scheduling methods might inadvertently concentrate maintenance operations when residual demand is particularly low, creating situations in which there is insufficient supply‐demand balancing capability and inertia to operate the power system properly. In this study, we present the development of a new method to schedule generator maintenance that ensure inertia and balancing capability constraints, and show its effectiveness.

Post-earthquake repairability enhancement of BRBFs considering isotropic hardening with self-centering braces

This research's aim is to develop a novel peak and residual deformation-targeted design (PRBD) procedure for enhancing BRBF's post-earthquake repairability with self-centering braces (SCBs). To this end, the peak and residual displacement demands of improved BRBFs (denoted as RBRBFs) were studied by single-degree-of-freedom (SDOF) system analyses, where the isotropic hardening features of BRBs were properly considered. The hysteretic behavior's effects of SCBs on the RBRBF's peak and residual displacements were comprehensively investigated. Artificial neural network (ANN) models were developed for predicting the displacement demands of RBRBFs. Based on the developed ANN model, the PRBD was subsequently proposed. Two design cases were presented based on the developed PRBD method with different design objectives. The designed RBRBFs' performance was investigated via dynamic analyses. It can be found from the results that RBRBFs can achieve the desired peak and residual displacements under design earthquakes, validating the effectiveness of the developed PRBD. Moreover, the excellent post-earthquake repairability of RBRBFs with the partial self-centering feature was confirmed by showing residual displacements lower than 0.2% under maximum considered earthquakes.

The role of masonry infills on the interstory drift demand of reinforced concrete frames

Most frame buildings, especially those with shear-type moment resistance frames, are affected by masonry infill panels, which change the mechanical properties of whole systems in a big way. In turn, seismic performance varies depending on the infill panel and frame interactions. In conventional structural design practice, such interaction has been overlooked. This study looks at the range of local displacement demands in shear-type frames with and without infill panels. Generic frames are developed by tuning the story stiffness and mass to produce a reasonable period range between 0.2 and 2.0 s. The masonry infill panels are simulated through equivalent diagonal struts. A Bouc–Wen-based hysteretic model is applied to incorporate the post-yielding hysteresis degradations of both columns and masonry panels. The hysteresis loop control parameter values are also given for incorporating masonry infill properties. The correlation analysis between the strength and stiffness of RC frames and masonry infills is supplied as an instrument for calibrating the hysteretic model. In the collection of records, there are a lot of near-fault ground motions, which puts a lot of seismic demands on the buildings. The modification factors via regression analysis are proposed using over 1254 nonlinear response history analyses. This modification factor is figured out by looking at the difference between the mean drift spectrum for a set of generic frames that are both bare and filled. The nonlinear analysis shows that residual drift demands can be reduced in the case of panel effects that exist for masonry-infilled mid-rise RC shear-type frames.

Global Residual Demand Analysis in a Deep Variable Renewable Energy Penetration Scenario for Replacing Coal: A Study of 42 Countries

This study analyzes the residual demand curves of 42 countries under five scenarios with varying variable renewable energy (VRE) levels to observe how replacing coal with VRE can alter the demand curve. Using 2018 demand data, the residual demand was calculated and analyzed by subtracting the VRE supply curve from the demand curve. The operational requirements for low-carbon load-following sources amid high VRE penetration are examined. Key findings indicate that substantial peak residual demand persists even with 70% energy from VREs, emphasizing the need for significant load-following resources. Transitioning to a 70% VRE scenario could reduce CO2 emissions by approximately 16.799 billion tons, advancing towards carbon neutrality. However, this benefit depends on maintaining grid stability, highlighting the importance of adequate load-following plants to manage VRE intermittency. Countries like Malaysia, South Korea, Tunisia, the UK, Japan, Indonesia, Thailand, and Libya face higher load-following demands due to specific renewable energy contexts. This study reveals varying renewable energy environments across countries, suggesting that a universal strategy for carbon neutrality and replacing coal may not be feasible. Each nation must develop its own approach to emission reduction, considering its unique conditions. This research emphasizes the urgent need for developing cost-effective, flexible, low-carbon load-following sources to enhance decarbonization potential globally.

Using Irradiation-Acquisition Interleaved Time-Integrated Imaging to Assess Delayed Luminescence and the Noise-Laden Residual Spontaneous Photon Emission of Yeast

Delayed luminescence from organisms informs oxidative stress that may be modulable by external stimulations. In the absence of external stress causing delayed luminescence, organisms may produce spontaneous ultraweak photon emission due to the residual oxygen demand. To better understand the oxidative state of an organism, it is desirable to acquire the delayed luminescence to reach the phase wherein the ultraweak photon emission resides. This, however, is challenging due to the significant difference in the order of magnitude of the photon counts between the two types of photon emission. Conventional time-gated measurement requires a high dynamic range to assess the noise-level photon emission, whereas simple long exposure can miss the kinetics of luminescence. There may be a compromise to be made between robustly acquiring the decay kinetics of the delayed luminescence and reliably acquiring the noise-laden spontaneous photon emission. We demonstrate an irradiation-acquisition interleaved time-integrated imaging approach that may enable the reliable acquisition of slow-decay delayed luminescence down to the level of ultraweak photon emission. Repetitive irradiation was interleaved with a gradually increased time of acquisition to assess the integrated time course of the post-irradiation luminescence. Such instrument configuration performed on yeast facilitated the use of time differentiation to assess the delayed luminescence down to the noise-level ultraweak photon emission at the expense of the total time of acquisition.

The importance of hazard-consistency when estimating seismic residual drifts in steel moment frames

Residual drifts are routinely used to assess the post-earthquake safety of buildings. Despite their importance, studies on the probabilistic assessment of residual drifts in multi-storey buildings with Steel Moment Resisting Frames (SMRF) are far less common than those dealing with their transient peak drift counterpart. More seriously, although residual drift prediction models have been developed with a particular broad ground-motion type or soil condition in mind, all models proposed to date remain oblivious to the central issue of hazard consistency. Hence, missing the causal connection between the seismic hazard level and the ground-motion suite used and potentially compromising the meaningfulness of their results. This oversight is expected to introduce a bias in the estimation of residual drifts but its magnitude has not yet been properly evaluated. In this paper, we evaluate and quantify the significance of these effects. To this end, we use the Conditional Scenario Spectra framework, which provides a set of realistic earthquake spectra with assigned rates of occurrences based on their spectral shape and intensity, thus preserving the critical relationship of hazard consistency. Nonlinear Response History Analyses (NRHA) of 24 deteriorating SMRFs under 816 ground-motion records are performed to construct a database of residual drift demands. These NRHA results are used to examine the residual drift trends and to construct benchmark residual drift hazard curves. An extensive feature selection process employing several Machine Learning (ML) algorithms precedes the development of hybrid data-driven predictive models. Finally, we compare our hazard-consistent predictions with currently available models and quantify the massive under- and over-estimations associated with previously proposed non-hazard-consistent assumptions.

Leveraging flexibility of residential heat pumps through local energy markets

The integration of variable renewable energy sources such as wind and solar energy has made demand-side flexibility a critical aspect for balancing the power grid during fluctuating power generation. In recent years, heat pumps have gained increasing attention for their flexibility potential. While demand response programs have been extensively discussed to leverage flexibility, the market-based approach of local energy markets (LEMs) requires more attention. LEMs provide a marketplace for local energy exchange, facilitating a more balanced energy system by harnessing the flexibility of heat pumps at lower costs. Therefore, this study examines the economic benefits of leveraging flexibility of heat pumps through LEMs and the problems that may arise. An agent-based simulation of LEMs with double-sided auctions is utilized to consider prosumer behavior, incorporate model-predictive control, and employ detailed modeling of energy devices. According to the findings, a district where 40% of households use heat pumps can reduce their annual cost by 5.1% through a LEM. The study identifies several factors contributing to the relatively small economic benefits, including high balancing costs, excessive taxes and network charges, uneven distribution of benefits, and seasonal fluctuations. Additionally, the study proposes daily demand charges to mitigate high residual demand peaks resulting from heat pumps. In conclusion, the study emphasizes the key role of heat pumps in achieving economic benefits through LEMs and highlights the regulatory framework changes required to effectively tackle the challenges faced by LEMs with a large share of heat pumps.

Global Aligned Structured Sparsity Learning for Efficient Image Super-Resolution.

Efficient image super-resolution (SR) has witnessed rapid progress thanks to novel lightweight architectures or model compression techniques (e.g., neural architecture search and knowledge distillation). Nevertheless, these methods consume considerable resources or/and neglect to squeeze out the network redundancy at a more fine-grained convolution filter level. Network pruning is a promising alternative to overcome these shortcomings. However, structured pruning is known to be tricky when applied to SR networks because the extensive residual blocks demand the pruned indices of different layers to be the same. Besides, the principled determination of proper layerwise sparsities remains challenging too. In this paper, we present Global Aligned Structured Sparsity Learning (GASSL) to resolve these problems. GASSL has two major components: Hessian-Aided Regularization (HAIR) and Aligned Structured Sparsity Learning (ASSL). HAIR is a regularization-based sparsity auto-selection algorithm with Hessian considered implicitly. A proven proposition is introduced to justify its design. ASSL is for physically pruning SR networks. Particularly, a new penalty term Sparsity Structure Alignment (SSA) is proposed to align the pruned indices of different layers. With GASSL, we design two new efficient single image SR networks of different architecture genres, pushing the efficiency envelope of SR models one step forward. Extensive results demonstrate the merits of GASSL over other recent counterparts.

Bioaugmentation Potential Investigation Using a Phenol Affinity Analysis of Three Acinetobacter Strains in a Multi-Carbon-Source Condition

Bioaugmentation potential and phenol substrate affinity in a multi-carbon-source condition for three Acinetobacter strains (Acinetobacter towneri CFII-87, Acinetobacter johnsonii CFII-99A and Acinetobacter sp. CFII-98) were demonstrated. First, the phenol biodegradation ability of the strains was analyzed in batch experiments with phenol as the sole carbon source. All strains degraded phenol at 100 and 500 mg·L−1 initial concentrations; the maximum specific growth rates were 0.59 and 0.30 d−1 for A. towneri CFII-87, 0.50 and 0.20 d−1 for A. johnsonii CFII-99A, and 0.64 and 0.29 d−1 for A. sp. CFII-98, respectively. For the two tested phenol concentrations, no lag phase was observed for the A. towneri CFII-87 strain, A. sp. CFII-98 presented 4 h and 8 h lag phase, while A. johnsonii CFII-99A presented 3 h and 12 h lag phases. Phenol carbon source dependency of the strains was tested in a multi-carbon-source condition (on phenol-rich synthetic wastewater), both for individual strains and for a consortium prepared as an equal mixture of the three strains. The strains A. towneri CFII-87 and A. sp. CFII-98 and the consortia degraded phenol in 16 h while there was no other significant carbon source consumption during the 48 h trial, as shown by the constant non-phenolic residual chemical oxygen demand (COD) and volatile suspended solids (VSS) concentration after the depletion of phenol. The strain A. johnsonii CFII-99A, however, consumed phenol within 24 h and a further decrease in non-phenolic COD and increase in biomass was also observed upon the depletion of phenol. The highest specific phenol removal rate of 282.11 mg phenol·g VSS∙h−1 was observed in the case of the strain A. towneri CFII-87, followed by A. sp. CFII-98, the consortium and A. johnsonii CFII-99A with 178.84, 146.76 and 141.01 mg phenol·g VSS∙h−1, respectively. Two bacterial strains (A. towneri CFII-87, A. sp. CFII-98) presented a strong affinity to phenol, utilizing it as a primary carbon source, and thus, their use in the bioaugmentation of wastewater bioreactors indicated the viable potential to increase the phenol removal rate of these systems.

Sodium hydroxide or tetramethylammonium hydroxide modified corncob combined with biodegradable polymers to prepare slow-release carbon source for wastewater denitrification

This study proposed a method to improve the bioavailability of artificially prepared carbon sources for the purpose of wastewater denitrification. This carbon source (named SPC) was prepared by mixing corncobs with poly(3-hydroxybutyrate-3-hydroxyvalerate) (PHBV), where the corncobs were pretreated by NaOH or TMAOH. The results of compositional analysis and FTIR showed that both NaOH and TMAOH degraded lignin, hemicellulose and their connection bonds in corncob, thus increased the cellulose content from 39% to 53% and 55%, respectively. The cumulative carbon release from SPC was about 9.3 mg/g and was consistent with both the first-order kinetic and Ritger-Peppas equation. The released organic matters contained low concentration of refractory components. Correspondingly, it showed excellent denitrification performance in simulated wastewater, and the total nitrogen (TN) removal rate was above 95% (influent NO3−-N was 40 mg/L) and effluent residual chemical oxygen demand (COD) was less than 50 mg/L.

How flexible electrification can integrate fluctuating renewables

To phase out fossil fuels, energy systems must shift to renewable electricity as the main source of primary energy. In this paper, we analyze how electrification can support the integration of fluctuating renewables, like wind and PV, and mitigate the need for storage and thermal backup plants. Using a cost-minimizing model for system planning, we find substantial benefits of electricity demand in heating, transport, and industry adapting to supply. In Germany, flexible demand halves the residual peak load and the residual demand and reduces excess generation by 80%. Flexible operation of electrolyzers has the most significant impact accounting for 42% of the reduction in residual peak load and 59% in residual demand. District heating networks and BEVs also provide substantial flexibility, while the contribution of space and process heating is negligible. The results are robust to restrictions on the expansion of the transmission grid.

The Oligopolistic Behavior of Kazakh and Russian Wheat Exporters in the South Caucasus: Evidence from a Residual Demand Elasticity Analysis

This study looks at whether Kazakh and Russian wheat exporters leverage their dominant share of the wheat markets in the South Caucasus to exercise market power. We apply a three-stage estimation for systems of simultaneous equations and Zellner’s seemingly unrelated regression to analyze residual demand elasticity. The results of both estimations provide empirical evidence of Russian market power in the wheat markets of the South Caucasus but no evidence of a Kazakh oligopoly. Russian exporters possess greater market power in Armenia than in Georgia. Market power depends on the presence of competitors in the destination market. The results show that Kazakh exporters restrict the market powers of Russian exporters in the Azerbaijani wheat market, while Russian exporters constrain the market power of Kazakh exporters in the Azerbaijani and Georgian wheat markets. Ukrainian wheat exporters are able to intervene in the market powers of Russian exporters in Azerbaijan and Georgia, while they restrict Kazakh oligopoly in the Georgian market. Some export restrictions imposed by wheat exporting countries significantly affected competition in wheat importing countries. Russia’s ongoing war against Ukraine and the blockade of Ukrainian ports on the Sea of Azov and the Black Sea are restricting wheat trade, severely damaging Ukraine’s competitiveness and export potential, and reducing food security in the South Caucasus, the Middle East, and North Africa.

Approximate Methods to Estimate Residual Drift Demands in Steel Structures with Viscous Dampers Designed by the DDBD Approach

Approximate Methods to Estimate Residual Drift Demands in Steel Structures with Viscous Dampers Designed by the DDBD Approach

An energy urgency priority based mobile charging scheme in Wireless Rechargeable Sensor Network

Recently, the use of Wireless Charging Vehicles (WCVs) to replenish energy for multiple nodes simultaneously has been a research hot spot in Wireless Rechargeable Sensor Network (WRSN). However, existing solutions either ignore the energy demand and its urgency, or only consider them partially in some stages, not during the whole process. There is no an overall solution in which distance and energy information with multiple metrics such as residual energy and demand urgency are jointly considered throughout the whole procedure. To address this problem, an Energy Urgency Priority based Mobile Charging Scheme (EUP-MCS) is proposed. Firstly, the WRSN is clustered by the algorithm of Residual Energy Priority based K-Means (REP-Kmeans++) which brings nodes with less residual energy closer to cluster center. Secondly, the WCV’s optimal docking site at each cluster is selected by the algorithm of Gradient Descent based Anchor Point Selection (GD-APS) according to the nodes’ energy demand weight. Finally, the WCV’s access path is determined by the algorithm of Energy Urgency Priority based Ant Colony System (EUP-ACS) which prioritizes the clusters with high energy demand urgency. Extensive simulation experiments show that REP-Kmeans++ reduces the intra-cluster charging time by 6.4% compared to K-Means, and GD-APS reduces the intra-cluster charging time by 16.5% compared to traditional methods. More comparison experiments reveal that our EUP-MCS scheme outperforms other solutions such as Energy-aware Anchor Point Selection (EAPS), Collaborative Charging Algorithm based on Network Density Clustering (CCA-NDC) and Modified MAX–MIN ant system and Equalization Strategy (MMES) in terms of total traveling distance, death node number, average residual energy and stability.

Why we must move beyond LCOE for renewable energy design

• Wind and solar generation devaluation occurs for grids with high renewable shares • This effect, due to intermittency, dramatically reduces the value of generated energy • Cost of Valued Energy can be used to account for this intermittency penalty • Minimizing COVE can improve VRE system design to better meet demand The inherent intermittency of wind and solar energy challenges the relevance of Levelized Cost of Energy (LCOE) for their future design which neglects the time-varying price of electricity. The Cost of Valued Energy (COVE) is an improved valuation metric that takes into account time-dependent electricity prices. In particular, it integrates short-term (e.g., hourly) wind and solar energy “generation devaluation”, whereby high wind and/or solar energy generation can lead to low, and even negative, energy prices for grids with high renewable penetration. These aspects are demonstrated and quantified using examples of two large grids with high renewable shares with three approaches to model hourly price: 1) residual demand, 2) wind and solar generation, and 3) statistical price-generation correlation. All three approaches indicate significant generation devaluation. The residual demand approach provides the most accurate price information while statistical correlations show that generation devaluation is most pronounced for the Variable Renewable Energy (VRE) that dominates market share (e.g., solar for California and wind for Germany). In some cases, the cost of valued energy relative to levelized cost can be 43% higher for solar (CAISO) and 129% higher for wind (ERCOT). This indicates that COVE is a much more relevant metric than LCOE in such markets. In particular, COVE is based on the annualized system costs relative to the annualized spot market revenue, and thus considers economic effects of costs vs. revenue as well as those of supply vs. demand. As such, COVE (instead of LCOE) is recommended to design and value next-generation renewable energy systems, including storage integration tradeoffs. However, more work is needed to develop generation devaluation models for projected grids and markets and to better classify grid characteristics as we head to a carbon-neutral energy future.

Integration of coagulation-flocculation process for improved solid–liquid separation of anaerobically treated textile effluent

ABSTRACT Anaerobic process is a promising technique for textile effluent treatment. However, poor solid–liquid separation and residual chemical oxygen demand (COD) in the treated effluent limit its scope for industrial applications. The study analyzed the nature of suspended microbial biomass in the anaerobically treated effluent and further targeted its feasible separation process. The size of the majority of suspended biomass (82%) particles ranged from 500 to 1000 nm. The suspended biomass showed 37.13% hydrophobicity and −15.4 mV zeta potential. The optimization of the coagulation-flocculation (CF) process showed 93% turbidity removal at 400 mg/L of alum, 8 mg/L of a cationic polymer and acidic pH 4, through one variable at a time approach. The optimized results using Central Composite Design (CCD) were experimentally validated, and 97% turbidity removal was recorded at 400 mg/L alum concentration, 12 mg/L of the cationic polymer, and pH 3.2. The results obtained were statistically significant, with a high regression coefficient (R2 = 0.97) at 95% confidence limits. The final treated effluent after CF showed 67% COD removal (from 862 ± 40 to 280 ± 14 mg/L) with 83% color removal (from 740 to 124 Hazen). Hence, the integration of the CF process with the anaerobic process could be a promising approach for textile effluent treatment.

Probability-based residual displacement estimation of unbonded laminated rubber bearing supported highway bridges retrofitted with Transverse Steel Damper

Predicting the post-earthquake residual displacement of isolated bridges is crucial in deciding their operational and recovery strategies after a seismic event. This study proposes a probability-based approach to estimate the residual displacement of a trilinear hysteretic system representing the unbonded laminated rubber bearing (ULRB) supported highway bridge retrofitted with transverse steel dampers (TSDs), considering the uncertainty of structural parameters and the variability of ground motions. To this end, this study systematically derives the quasi-static residual displacement of a trilinear hysteretic system, and presents the statistical distribution model for the related structural parameters, especially for the pier height and the TSD model parameters. After that, the dynamic residual displacement property of the ULRB supported highway bridge retrofitted with TSDs is investigated considering the uncertainty of various parameters, including model parameters of TSD and ULRB, the mass of superstructure, the height of piers, as well as the ground motions with and without pulse. The maximum possible residual displacement of the trilinear hysteretic system is then related to its quasi-static residual displacement and peak displacement demand. Finally, an innovative probability-based approach is developed in this study to estimate the conditional probability of the dynamic residual displacement of the trilinear system exceeding a threshold at the given ground motion intensity, and this innovative approach is further validated by comparing it with the incremental dynamic analysis (IDA) method. Results show that the dynamic residual displacement of the TSD retrofitted ULRB supported highway bridge system is random, and the ratio between the dynamic residual displacement and the maximum possible residual displacement approximately satisfies a uniform distribution bounded by zero and one. The probability-based approach can obtain a close result with the IDA method and significantly reduce computational efforts. This research represents a first step toward developing a rapid post-earthquake assessment approach for seismically isolated bridges and the seismic design of these bridges considering the residual displacement.

Price-Cap Regulation, Price Elasticity of Demand, And Quality of Service

Price-cap regulation is aimed at limiting price increases by the significant market powers and prevent the decrease in consumer surplus. Therefore, studies on price-cap regulation have implicitly neglected the theoretical possibility that firms might lower their prices to increase revenue when the price elasticity of demand exceeds 1. Using Chunghwa Telecom (CHT), the largest mobile phone service provider in Taiwan, as a case study, we estimated the price elasticity of residual demand faced by CHT and found that it was higher than 1. Therefore, CHT’s continual lowering of price, which improved its revenues during 2010–2013, was reasonable. Because telecommunication service providers are characterized by high fixed costs and low variable costs, these firms have the incentive to increase revenue and subsequently maximize their profit. However, lowering the prices attracts more subscribers while the capacity remains unchanged, thereby congesting communication channels and reducing service quality. For public utility regulators, our results reiterate that price-cap regulation should be combined with quality control, particularly when the price elasticity of demand exceeds 1.

Changes of China’s soybean import market power and influencing factors

ABSTRACT Whether Chinese soybean importers can keenly grasp the changes of different source countries’ market power, will directly affects the safety and cost of soybean import. Based on the analysis of changes in China’s soybean import trade structure, this paper utilizes monthly data from January 2009 to December 2021, combines with the residual demand elasticity model, to compare and analyse the market power of the three major source countries. The results show that although the United States is no longer China’s largest soybean import country, its market power in China’s soybean import market has not been affected, while Argentina’s market power is gradually increasing. Although Brazil has grown into the first source of China’s soybean imports, it has only weak market influence. In addition, China, as the world’s largest buyer, doesn’t have market power. China’s soybean import price is mainly affected by futures prices, domestic soybean prices, international crude oil prices, Sino-US trade frictions and COVID-19 epidemic.