Optimal Demand Research Articles

Fabrication, characterization and performance analysis of a two-step arsenic bio-filter column using Delftia spp. BAs29 and fired red mud pellets.

Arsenic (As) is considered to be a grave inorganic pollutant, contaminating major aquifers worldwide. In this study, a two-step approach has been designed to combat this toxic metalloid by combining a highly efficient As (III) oxidizing bacteria; Delftia sp. BAs29 and fired red mud pellets to remove the total As from groundwater including both As (III) and As (V) ions. The maximum capacity of As (III) oxidation by Delftia sp. BAs29 was seen to be 95.65% for 500ml of As contaminated groundwater using an optimized As (III) concentration of 300ppb and 6.5g of bacterial cell mass for 7days. The second step indicated the maximum As (V) adsorption capacity by the stacked red mud pellets to be 97.91% for 500ml of As contaminated groundwater using the optimized pore size of 106-125μm for 7days. The efficiency of As removal increased to 98.76% at a flow rate of 50ml/h on combining of both the steps. In addition, the morphological properties, chemical composition, and the crystal structure of the As (V) adsorbed red mud pellets were characterized. The techno-economic feasibility of this entire unit was studied using SuperPro 10 software to estimate its optimal demand and potential. Hence, it is believed that scaling up of this two-step bio-filter column can serve as a potent filtration unit to eliminate As, both at the household and industrial level in the near future.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Optimal asset allocation for commodity sovereign wealth funds

This paper studies the dynamic asset allocation problem faced by an infinitively lived commodity-based sovereign wealth fund under incomplete markets. Assuming that the fund receives a non-tradable stream of commodity revenues until a predetermined date, the optimal consumption and investment strategies are state and time-dependent. Using data from the Norwegian Petroleum Fund, we find that the optimal demand for equity should decrease gradually from 60% to 40% over the next 60 years. However, the solution is particularly sensitive to the correlation between oil and stock price changes. We also estimate wealth-equivalent welfare losses, relative to the optimal rule, when following alternative suboptimal investment rules.

Ku-band analog photonic down-conversion link with coherent I/Q image rejection and digital linearization.

Analog photonic down-conversion links have been widely used in radar, electronic warfare, and satellite communication systems. Aiming at the optimization demands of the link performance, we demonstrate and experimentally verify a Ku-band photonic down-conversion link based on coherent in-phase/quadrature (I/Q) image rejection and digital nonlinear compensation. The image-rejection ratio at 17.5 GHz is measured to be 47 dB. After digital processing, the image intermodulation distortions (MMD) and the intermodulation distortions (IMD3) are suppressed by 18.1 dB and 10.9 dB, respectively. The corresponding spurious-free dynamic range (SFDR) reaches 108.83 dB·Hz2/3.

ML2-enabled Condition-based Demand, Production, Inventory, and Maintenance Planning

Production planning and control is pivotal to meeting customer demand and maximizing profit. At the same time, machine breakdowns compromise these goals, which can be tackled with a good maintenance strategy. Here, advances in condition-based maintenance and prognostics and health management allow predicting the health state of production machines through sensor data and prescribing optimal demand, production, inventory, and maintenance plans. Here, machine learning (ML) is promising for accurate health predictions using sensor data and decision-making in complex, highly dynamic production environments. Thus, in this work, two ML algorithms are applied. First, a data-driven regression algorithm predicts the health of a machine. This forecast is forwarded to a reinforcement learning algorithm (i.e. proximal policy optimization, recently made famous by its application within ChatGPT) to optimize demand, production, inventory, and maintenance plans. A computational study shows excellent performances of the ML-based health prediction and planning algorithms, which surpass traditional maintenance strategies.

Research on Comprehensive Control of Power Quality of Port Distribution Network Considering Large-Scale Access of Shore Power Load

In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load, a method of power quality management of port distribution network is proposed. Based on the objective function of the best power quality management effect and the smallest investment cost of the management device, the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed. Based on the balance between the economic demand of distribution network resources optimization and power quality management capability, the power quality of distribution network is considered comprehensively. The proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution networks. The simulation results show that the proposed optimization method can maximize the power quality management capability of the port distribution network, and the proposed optimization algorithm has good convergence and global optimization finding capability.

Optimal Demand Shut-offs of AC Microgrid using AO-SBQP Method

Microgrids are increasingly being utilized to improve the resilience and operational flexibility of power grids, and act as a backup power source during grid outages. However, it necessitates that the microgrid itself could provide power to the critical loads. This paper presents an algorithm named alternating optimization based sequential boolean quadratic programming tailored for solving optimal demand shut-offs problems arising in microgrids. Moreover, we establish local superlinear convergence of the proposed approximate Boolean quadratic programming method over nonconvex problems. In the end, the performance of the proposed method is illustrated on the modified IEEE 30-bus case study.

A Consensus-Based Distributed Temperature Priority Control of Air Conditioner Clusters for Voltage Regulation in Distribution Networks

High penetration of Photovoltaic (PV) to the distribution network may bring under-voltage and over-voltage issues, limiting the PV hosting capacity. Air conditioners (AC) in grid-interactive buildings can support voltage regulation by manipulating flexible energy consumption. This paper developed a novel voltage control strategy to regulate the AC clusters’ on/off states for distribution network voltage regulation under high PV penetrations. The novelty lies in the distributed formulation of temperature priority-based on/off control (TPC) of AC clusters and the strategic selection and permutation of demand response technologies, including the real-time optimal demand response resources dispatch, distributed sensing of ACs based on average consensus algorithm, and the local implementation of TPC strategy and trial calculation scheme for flexibility capacity estimation. Finally, the distributed TPC is validated to be effective for system rebalancing with no comfort violations and an acceptable ON/OFF switching frequency. The theoretical and numerical analysis also proves its scalability and robustness to communication delays and link failures. It is then incorporated into a novel hierarchical control framework for smart grid voltage control in a four-bus three-phase test grid, considering the voltage sensitivities to power injections in different locations and phases.

Pelican Optimization Algorithm for Optimal Demand Response in Islanded Active Distribution Network Considering Controllable Loads

Pelican Optimization Algorithm for Optimal Demand Response in Islanded Active Distribution Network Considering Controllable Loads

Potential of finite element method to optimize the olive harvester machine (hand‐held rotary harvester)

AbstractThe mechanical damage of olive caused by harvesting machines is one of the main reasons for the quality reduction of produced olive oil. The hand‐held rotary harvester (RH) is a machine that is used in small orchards extensively. The present paper focuses on utilizing the finite element method (FEM) to define the percentage of bruising damage by RH with two different materials of the head component (Ethylene Propylene Rubber and Neoprene). Three cultivars, ripening stage, and rotational speeds of RH head components were considered for experimental and simulation methods. The controlled impactor device was developed to validate the simulation process. The simulation and experimental results showed the Ethylene Propylene Rubber head produced less stress and bruise volume than the Neoprene head. Moreover, the quality characteristics of extracted olive oil, including free fatty acid, peroxide value, K232 and K270 coefficients, total chlorophyll, total carotenoid, total phenol, and total flavonoid were measured. Finally, the correlation of quality characteristics of olive oil, measured bruise volume and simulated was investigated using linear model and principal components analysis.Practical applicationsDespite the advancement of horticulture industries, the demand for optimization of agricultural machinery to decrease mechanical damage during the harvest process exists. The hand‐held olive harvester causes the bruise phenomenon reduction, leading to decrease quality of extracted olive oil. In this research, the time‐dependent finite element method based on dynamic‐explicit was utilized to simulate the harvest process. The results show that using the numerical and statistical methods, the producer factories of olive harvester machines can optimize the devices to enhance the quality of harvested olive and extracted olive oil.

Optimized Colorectal Cancer Patients’ Management During the COVID-19 Pandemic: Triage and Treatment

# Background The outbreak of coronavirus disease 2019 (COVID-19) has created significant challenges in the management of oncology patients, including patients with colorectal cancer (CRC). We suspect that the COVID-19 pandemic had a major impact on the number of CRC inpatients and outpatients, which might leave many CRC patients unable to get timely medical treatment. At the time, the most important task was to satisfy the imperious demand for rapid optimization of processes and the development of efficient and effective triage and treatment strategies, as well as emergency distant clinical reasoning. # Methods The number of outpatients and inpatients, as well as surgeries performed in Shanghai East Hospital from December 2019 to February 2020 were collected. Using December 2019 data as the baseline status before the pandemic, the changes during this period were analyzed which can reflect the impact of COVID-19 on the treatment of CRC patients. In addition, a triage system and management strategy for patients with CRC during COVID-19 were designed and implemented. To evaluate their effectiveness, we assessed COVID-19 infection rates among CRC patients in relation to total patients and healthcare staff. # Result Compared with the pre-COVID-19 period (December 2019), a drastic decline in the number of outpatient visits (2,789 to 120) and inpatient hospitalizations (207 to 50) for all the CRC patients, as well as in non-emergency colorectal surgeries (133 to 23), was observed in February 2020 at our hospital. # Conclusion A multidisciplinary triage strategy aligned with regional guidance and digital, artificial intelligence (AI)-technology solutions can help increase the efficacy in patient management, allow efficient access to care and reduce the incidence of COVID-19 among CRC patients.

A novel method for eliminating the exponential growth of computing optimal demand response events for large-scale appliances re-scheduling

The electric grid is undertaking a change of paradigm. New requirements such as the high power loads of Electric Vehicles’ charging are putting pressure on grids, and the popularisation of the figure of the prosumers, are creating new needs on the management of the system. Although the investment on infrastructure is one of the ways of alleviating the arrival of this new loads, the existence of smart controls, actuators and smart appliances opens also the door for demand modification. The so called Demand Response Events are strategies to reallocate the loads of the consumers, so the power demand is shifted towards times less critical for the grid. Some smart appliances can be considered as shiftable . The operation of them can be rescheduled to other times and still provide their services. On this paper we suggest an optimisation methodology consisting on a parallelisation method together with a generic algorithm that would allow an overlooking controller (such as aggregator or DSO) reduce the power peaks even with a large number of devices to control. The research here presented has looked for the smallest group size at which the optimisation is more effectible done in parallel groups rather than on a single large set. With this, we eliminated the exponential growth of the computational effort that the optimisation algorithm would need to do organising all devices in one go. The method achieves a computational time reduction of 80%, and it is only worsening the reduction of peak by 2.8% compared to an optimisation with a complete set.

Evaluating the effects of demand response programs on distribution cables life expectancy

• Evaluating the effects of DRPs on the LOL rate reduction of distribution cables. • Evaluating the effects of DRPs’ tariffs on the LOL rate of distribution cables. • Proposing a new economic benefit index. • Prioritizing various models of DRPs based on AHP method. • Maximizing utility's and customer's total benefits by determining the optimal model. Recently due to economic issues, asset management has taken more attention all over the world. Distribution cables are one of the most expensive assets of the networks. The life of a cable extremely depends on the power flowing through the cable. On the other hand, demand response programs are known as the one of the effective solutions for managing networks’ power consumption. They can affect the power flowing through the distribution cables. However, the impacts that the demand response programs might have on the life expectancy of the distribution cables are generally neglected in previous research studies. So, this paper proposes a new approach to evaluating the effects of demand response programs on the life expectancy of distribution cables. Simulations have been carried out on a realistic distribution network, and the results are investigated considering various models of the demand response program and different levels of their tariffs. The results show a significant economic benefit due to distribution cables’ life extension resulted from demand response programs implementation, despite differences among various models. However, the benefit's scale dramatically depends on the model of the demand response program and the level of tariffs. Also, in order to select the optimal demand response model, economic analysis is conducted from both utility's and customer's perspectives. For this purpose, a new economic benefit index is proposed, and the best solution is determined by the distribution system's regulator based on an Analytical Hierarchy Process so as to it can satisfy all the stakeholders of demand response programs.

Data Protection, Cookie Consent, and Prices

A legislative process is currently ongoing in the European Union to supplement the 2018 General Data Protection Regulation regarding ePrivacy regulation. The supplement is intended to complete the European data protection policy in significant areas. One addition would be for service providers on the Internet, who currently obtain the consent of their users via an opt-out provision, to always provide a paid alternative without disclosing data. This procedure is essentially aimed at overcoming “cookie consent fatigue”, which can be observed in many cases. A simple economic exchange model shows that users, as data subjects, are basically faced with the choice of paying a monetary price for a service that will also preserve their privacy or using Internet services “for free” while negating data privacy preferences. The individual demand for data privacy coincides with the socially optimal demand only if there is effective competition in the markets for data and Internet services and if users are sufficiently informed. In an online laboratory experiment with students of the Leuphana University of Lueneburg, a between-subjects design was applied in which the control group only had the option to either “pay” for the use of the artificial intelligence DeepL via cookies by surrendering data or to abstain from the service altogether, with the two treatment groups additionally given the option to use DeepL in exchange for a monetary fee so that privacy was not violated. To be tested was whether the “monetary price for privacy” option better reflected users’ privacy preferences than the current cookie opt-out solution. The results show that it was much less common for DeepL to be remunerated with the disclosure of data and less common for DeepL to be waived entirely.

A hybrid Monte Carlo-discrete ordinates method for phonon transport in micro/nanosystems with rough interfaces

In recent years, the rapid development of micro/nanosystems raises the demand of thermal optimization of the new designs, and thus the accurate and efficient prediction of phonon transport in mesoscopic systems with complex interfaces is required. This work proposes a hybrid Monte Carlo-discrete ordinates method (MC-DOM) for steady-state phonon transport in such systems by developing an algorithm to exchange information between MC and DOM subdomains. Based on the phonon Boltzmann transport equation which is physically compatible with the system scale, our formulation exploits the computational efficiency of DOM in the bulk region with flexibility of MC near complex interfaces. The cross-plane phonon transport through the thin films is considered first as the benchmark for method verification, whose results agree well with those calculated by numerical and analytical solutions. It is noteworthy that compared to MC as a pure particle method, the hybrid method runs faster over a hundred of times for the temperature calculation when high-precision results are needed. The hybrid method is then applied to simulate the in-plane phonon transport through the double-layer thin films with rough interfaces. It is found that for rectangular interfaces with different roughness, the interfaces perpendicular to the heat flux will cause a larger thermal resistance than the parallel ones. Our hybrid method enriches numerical tools for mesoscopic phonon transport simulation, and is helpful to reveal the mechanisms behind and optimize the heat transport in micro/nanosystems with complex geometries.

Optimal demand response programs selection using CNN‐LSTM algorithm with big data analysis of load curves

One of the problems in implementing DRPs is the lack of sufficient understanding of consumers’ behaviour when implementing DRPs. This paper compares consumers’ load patterns annually by the improved Weighted Fuzzy Average (WFA) K-means clustering method. According to the collected data, DRPs are discussed annually using a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). To make CNN-LSTM a practical algorithm for executing DRPs, Time Series Prediction (TSP) operations must be performed, and input data must be memorized. Finally, according to the TSP done for the obtained data, the implementation of DRPs in practice is facilitated. Then, the DRPs executed with the Deep Learning (DL) model and the power model will be prioritized by Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method, and decision indicators will be determined and weighted using the Shannon entropy method. The numerical studies section shows that the CNN-LSTM algorithm is able to simulate DRPs with the Mean Absolute Percentage Error (MAPE) almost below 1% in the residential cluster and the highest MAPE in the commercial cluster of 15%. Nevertheless, this algorithm could easily give the same answer as the DRPs power model in the optimal selection of programs.

Information sharing and carbon reduction strategies with extreme weather in the platform economy

Extreme weather, cap-and-trade policy, and demand information asymmetry between firms and e-commerce platforms have huge impacts on their operation strategies. In these contexts, platforms face the challenge of whether sharing demand information and firms face the challenge of determining optimal pricing and carbon reduction strategies. To address these challenges, our paper develops four theoretical models and presents some insights. The main results imply that in the case of low actual demand potential, platforms should share demand information with firms. However, in the case of high actual demand potential, platforms' information sharing strategies will be affected by the carbon-trade price and the difference between the high and low actual demand potential. Moreover, the results also show that extreme weather will have a complicated impact on the optimal demand information sharing strategy. It is interesting that non-disclosure of information in some cases benefits the environment. Main results are still valid when considering extreme weather causes a rise in product carbon emission and firms selling two differentiated products. Besides, we find that platforms' choice to share information is always beneficial to firms in the absence of extreme weather, however, this result cannot hold under extreme weather.

Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm

Thermal load is an important type of demand response (DR) resources to maintain the power balance of regional electricity-heating energy systems. However, the diversity of power-consuming patterns and time series coupling of production processes arise challenges for precisely tracking the scheduled command of industrial and domestic thermal loads. This paper proposes a feasible solution to achieving collaborative scheduling of diversified thermal loads meanwhile optimizing the tracking performance of scheduled power, via a mixed integer linear programming (MILP) based decomposition algorithm. The power-temporal granularity is firstly introduced and modeled to characterize the available regulated capacity of diversified thermal loads in each power regulation event. Then, the time series coupling model among multiple thermal devices is established to describe the coupling relationship in production processes. On this basis, the diversified thermal loads are co-regulated in the considered system to accomplish the decomposition objectives and without taking up extra generation and storage regulation resources. Case studies based on real data of a regional thermal loads are conducted, which demonstrates the feasibility and effectiveness of proposed method in improving comprehensive benefits and reducing power deviation.

Optimal demand for medical and long-term care

For the population over 65, long-term care (LTC) expenditure constitutes a considerable share in health care expenditures. In this paper, we decompose health care into medical care, intended to improve one’s state of health, and personal care required for daily routine. Personal care can be either carried out autonomously or by a third party. In the course of aging, autonomous personal care is gradually substituted by LTC. We set up a life-cycle model in which individuals are subject to physiological aging, calibrate it with data from gerontology, and analyze the interplay between medical care and LTC. In comparative dynamic analyses, our theory-based approach allows us to causally investigate the impact of better health and rising life expectancy, triggered by higher income and better medical technology, on the expected expenditures for LTC in the future. We predict that a one percentage increase in life expectancy is associated with a 1.75-percentage increase in expected LTC expenditure. In terms of present value at age 20, this elasticity declines to about 1.0 percent. Even when considering different magnitudes and compositions of shocks in medical technology and income, we find that these elasticities remain remarkably stable.

Optimization of zigzag parameters in printed circuit heat exchanger for supercritical CO2 Brayton cycle based on multi-objective genetic algorithm

The printed circuit heat exchanger (PCHE) shows promise for applications in the supercritical carbon dioxide (S-CO2) Brayton cycle due to its great resistance to high temperature and pressure. The zigzag channel is the typical channel configuration of PCHEs containing structural parameters such as pitch, bend angle, and bend radius that can significantly influence flow and heat transfer performance. In this study, the pitch, bend angle, and bend radius are selected as the design variables, while the pressure and heat exchange capacity are considered as the optimization objectives. Based on the non-dominated sorting genetic algorithm-II (NSGA-II), optimization work is conducted to improve the performance and size of the zigzag channel over a wide range of structural parameters. To predict local flow and heat transfer performance, the surrogate model embedded in the one-dimensional analysis code is proposed using the artificial neural network (ANN) method and trained by 144 groups of three-dimensional simulation results. The results calculated by the one-dimensional analysis code show that both the bend angle and bend radius can significantly affect the performance of the printed circuit heat exchanger, while the bend radius plays a more important role on the pressure drop. However, the effect of the pitch on the zigzag channel is relatively small. The optimization results indicate that the appropriate core length of the zigzag channel varies with the design of the outlet temperature. For the 0.4 m core length suitable for the 306 K outlet temperature, the size is 20% less than for a 0.5 m core length with similar flow resistance, and the pressure drop is 66.3% less compared to that of the 0.3 m core length. In addition, the zigzag channel with hybrid structural parameters is proposed and compared to the conventional zigzag printed circuit heat exchanger. The optimization results indicate that the maximum difference between the two objectives is only 4%. Thus, the zigzag channel with constant structural parameters can satisfy the demand for optimization with a simpler design.