Load Shift Research Articles

Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.

Regional Operation of Electricity-Hythane Integrated Energy System Considering Coupled Energy and Carbon Trading

The deepening implementation of the energy and carbon market imposes trading requirements on multiple regional integrated energy system participants, including power generation plants, industrial users, and carbon capture, utilization, and storage (CCUS) facilities. Their diverse roles in different markets strengthen the interconnections among these subsystems. On the other hand, the operation of CCUS, containing carbon capture (CS), power-to-hydrogen (P2H), and power-to-gas (P2G), results in the coupling of regional carbon reduction costs with the operation of electricity and hythane networks. In this paper, we propose a regional economic dispatching model of an integrated energy system. The markets are organized in a centralized form, and their clearing conditions are considered. CCUS is designed to inject hydrogen or natural gas into hythane networks, operating more flexibly. A generalized Nash game is applied to analyze the multiple trading equilibria of different entities. Simulations are carried out to derive a different market equilibrium regarding network scales, seasonal load shifts, and the ownership of CCUS. Simulation results in a 39-bus/20-node coupled network indicate that the regional average carbon prices fluctuate from ¥1078.82 to ¥1519.03, and the organization of independent CCUS is more preferred under the proposed market structure.

Study on the Influence of Different Slot Sizes on the Flow Field of Transonic Compressor Rotors

Blade slotting technology is an effective measure to improve the flow structure on the suction surface of a blade and enhance the performance of turbomachinery. To investigate the impact of various slot sizes on the flow field of a single-stage transonic compressor rotor, seven kinds of slot schemes were designed and calculated by numerical simulations. The results show that the above slotting schemes significantly enhance the stability margin of the compressor. In particular, the slotting scheme H9W3 increases the surge margin by 60.9% and slightly reduces peak efficiency by 0.3%, with an almost identical maximum pressure ratio. Slotting promotes high-energy fluid to generate jets from the slot located at the exit of the suction side, effectively controlling blade surface flow separation and reducing channel blockage. Square slots are more effective than elongated slots for controlling separation when using differently shaped slots with equal areas. Increasing slot area gradually decreases outlet total pressure at a constant aspect ratio. A slight increase in the overall blade load causes a backward shift in the front portion load.

Estimation of Domestic Load Profile for Effective Demand-Side Management

In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24- hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

MANAGEMENT OF SCALABILITY IN CLOUD-BASED APPLICATIONS

The following is an abstract of the article. The article presents an analysis of the challenges associated with monitoring and managing the scalability of a cloud application. To this end, a module for monitoring and managing the scalability of a cloud application has been developed as part of this study. The development process included the introduction of automatic scaling, and monitoring using Prometheus and Grafana, which allows for a high level of availability and resource efficiency. The study comprised a series of phases, including requirements analysis, system design, development, testing, and evaluation. Consequently, the system's performance, stability, and capacity to scale in response to fluctuating workloads were enhanced. The module exhibits a high degree of adaptability to changes in system requirements and load, which is a crucial attribute for the dynamic development of business applications. This solution assists in optimizing the allocation of resources and reducing infrastructure costs. The project has been found to fully meet the set goals and objectives, as well as the requirements for effective resource management of the Amazon Web Services cloud platform using Terraform, Prometheus, and Grafana. The practical value of the developed module is evidenced by a significant improvement in resource efficiency, service stability and cost optimisation. The module design has been subjected to rigorous testing and has been successfully implemented in a test environment, thereby demonstrating the sustainability and efficiency of the developed solution. The experience gained in the implementation and operation of this solution may prove useful for further expansion and optimization of cloud solutions in other projects and companies specializing in the provision of cloud solutions. The findings of this study were validated in a test environment at an IT company with a specialization in cloud technologies. The objective was to ascertain the functionality and efficiency of the developed module in a real-world context of cloud infrastructure operation. The testing process entailed the configuration of the module on pre-existing cloud infrastructure systems, its integration with Prometheus and Grafana for monitoring purposes, and the execution of a series of stress tests designed to assess the module's scalability. As a result of this testing, a number of critical points were identified that required further optimization. The results of the study and the issues identified during the project testing have enabled the identification of several areas for further improvement and development of the system. First and foremost, the optimization of automatic scaling algorithms represents a crucial avenue for improvement. The development of these algorithms should be oriented towards utilizing historical monitoring data to anticipate potential shifts in system load. Another pivotal area for enhancement is the precision of monitoring systems. The integration of supplementary tools and the expansion of existing monitoring systems' functionality will facilitate the acquisition of more comprehensive insights into the system's condition. This, in turn, will facilitate the expedient identification and eradication of potential issues.

Coordinated scheduling optimization of building integrated energy system with flexible load

Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side.

Delirium event and associated treatment modifications among older adults with Alzheimer's disease: An interrupted time-series analysis of Medicare data.

Both Alzheimer's disease (AD) and deliriogenic medications increase the risk of delirium in older adults. This study examined the association between delirium and the subsequent monthly use of anticholinergic, sedative, and opioid medications in the 1 year after delirium in older adults with AD. This comparative interrupted time series analysis involved adults (aged 65 years and older) with a diagnosis of AD initiating on cholinesterase inhibitors (ChEIs) based on 2013-2017 Medicare data. Separate patient-level segmented regression models were used for each outcome to evaluate changes in the cumulative anticholinergic burden (CAB), sedative load, and opioid load after the delirium/index event using a 12-month baseline and follow-up period among patients who had a delirium event and those without delirium (control group). Propensity score-based stabilized weights were utilized to balance baseline factors in the delirium and control groups. The study included 80,019 older adults with AD with incident ChEI use; 17.11% had delirium. There was an immediate decline in monthly CAB after the delirium event (mean estimate -0.86, p-value: 0.01) compared to the control group. A similar decline was observed when examining the sedative load (-0.06, p-value: 0.002) after the delirium event. However, there was no decline in opioid load (-0.50, p-value: 0.18). In the long term, CAB (0.13; p-value: <0.0001), sedative load (0.01; p-value: <0.001), and opioid load (0.07; p-value: 0.006) increased over the 1-year post-delirium period in the delirium group compared to those without delirium. This study found the burden of deliriogenic medications over the 1-year follow-up showed increasing trends in older adults with AD, even though there was some level shift in CAB and sedative load after the delirium event.

Optimization clearing strategy for multi-region electricity-heat market considering shared energy storage and integrated demand response

As a new type of energy storage, shared energy storage (SES) can help promote the consumption of renewable energy and reduce the energy cost of users. To this end, an optimization clearing strategy for a multi-region electricity-heat joint market is proposed with consideration of SES and integrated demand response (DR). Firstly, the concept of shared energy storage station (SESS) is proposed, its business operation model is analyzed and its advantages over traditional energy storage are compared. Secondly, to exploit the potential for user flexibility, an integrated DR model that includes shiftable, transferable, interruptible electricity and heat load is constructed. Then, a multi-region electricity-heat joint market clearing model is constructed considering SESS and integrated DR. Finally, a three-region electricity-heat joint market connecting the external power grid, gas grid, and the SESS is used as an example. A comparison is made between the configuration of independent energy storage in each region and the configuration of SESS, which concludes that the introduction of the SESS and integrated DR can reduce the energy cost of users and improve the utilization rate of the energy storage facilities.

Design and Experimental Testing of Extended-Range Power Supply System for 15 Horsepower Electric Tractor

Electric tractors have many advantages, including high torque, excellent controllability, energy efficiency, a simple structure, and an electric interface for expansion. However, a significant limitation lies in their endurance. This study presents the design of an extended-range power supply system to ensure continuous endurance for an electric tractor. The objective is to provide a continuous power source for our self-developed electric tractor while preserving the benefits of electric propulsion. Extended-range power systems utilize a primary mover, typically an oil-fueled internal combustion engine, to drive the generator for electricity generation, and the generated AC-form electricity is subsequently converted into stable DC bus voltage by a power electronic converter. The hardware and control design of an extended-range power supply system are finalized and validated through experimental trials. The results demonstrate the system’s capability to sustain stable DC bus voltage amidst disruptions such as sudden load shifts and fluctuations in the prime mover’s speed. Even with a 50% sudden load change, the voltage drop is within 12% and can recover to ±3% within 4 s. The extended-range can be used alone without a battery to power the electric tractor, or it can used in parallel with other extended ranges or batteries for power sharing thanks to the droop control ability.

Implementation of 4-bit Universal Shift Register using Reversible Logic

The field of digital logic design is increasingly exploring reversible logic due to its potential in minimizing power dissipation, an essential criterion in low-power applications. This paper presents the design and implementation of a 4-bit universal shift register (USR) using reversible logic gates. The design leverages Fredkin gates, multiplexer (MUX), and D flip-flops to achieve functionality with minimal power consumption. Simulation results demonstrate the efficiency and effectiveness of the proposed design. Reversible logic is essential to low-power digital design and quantum computing because it makes it possible to reconstruct input signals from output signals, which reduces heat generation and information loss. The suggested shift register can be used for hold, parallel load, and left and right shift operations. To implement the functions of the shift register, we used a variety of reversible gates, including the Toffoli and Fredkin gates. When the architecture is contrasted with traditional non-reversible shift registers, it shows notable gains in reduced gate count and power efficiency

Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system

The expansion of electric vehicles (EVs) and renewable energy (RE) are the two major strategies countries are adopting to achieve energy transition. However, the discrepancy between intermittent RE generation and EV charging load may impose burden on the RE-dominant power grid of the future. For the issue, Time-of-Use (ToU) pricing for EV charging has been widely discussed or adopted as its potential is well acknowledged by previous studies. While ToU mechanism has been primarily based on drivers' price-responsive behavior, this research highlights that EV charging decisions are influenced by various factors beyond price, such as time of a day, charger accessibility and waiting time. Here, we conducted the discrete choice experiment to measure drivers' preferences for EV charging, and developed an EV charging behavior model which incorporated drivers' situational-responsiveness as well as price-responsiveness. Also, the model was used to design optimal ToU tariffs to minimize net-load variation. The results showed that strategic ToU tariffs can shift EV charging load, but achieving desirable load shifts requires a significant price gap. Additionally, combining ToU pricing with strategic deployment of charging infrastructure can effectively shift EV charging load, reducing RE curtailments by 22.14%, and LNG generation and carbon emissions by 10.12%, compared to the Business-as-Usual (BaU) scenario with current tariff rates. Thus, this study highlights the importance of flexible EV charging pricing and the importance of considering charging infrastructure deployment when designing EV ToU pricing.

Load shift keying communication techniques in implantable devices.

Inductive links represent a highly promising avenue for both powering and communicating medical implants. Yet they encounter challenges such as constrained communication distance and limited data rate. In Load Shift Keying (LSK), a switch in the secondary side of the inductive link can be placed in parallel with the load (Short-Circuit Technique - SCT), in series with the load (Open-Circuit Technique - OCT), or both (Dual Technique - DLT), to vary the impedance of the secondary. Hence, the impedance reflected to the primary side changes and is used to transmit information externally from the implant. Among these, DLT is a novel LSK technique proposed in this work, which becomes independent from the load on the implant side. This study compares these three methods, confronting measurements to simulations. The evaluation focused on variations in coil distance and load. The proposal is illustrated in the case of an implantable gastric stimulator, with specific constraints in secondary coil size and power requirements. The newly developed DLT consistently outshone SCT and OCT in extending the operational range of communication, registering a maximum modulation index of 0.797 and a bit error rate below 10- 7 at an operating distance of 95mm through the air. Its load-independent characteristic allowed DLT to surpass the performance of SCT and OCT, which were each advantageous under high and low loads, respectively. All these results are confirmed by a LTSpice simulation. Consequently, the communication techniques put forward in this work mark a significant progression in medical implant communications, enhancing coil-to-coil operational distance while adhering to a low carrier frequency.

Compensation Mechanism of Controllable Load Shifting during Peak-Down Period Based on Revenue Balance Method

Compensation Mechanism of Controllable Load Shifting during Peak-Down Period Based on Revenue Balance Method

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

Transactive energy framework in fuel cell based multi-carrier energy hubs based on conditional value-at-risk

The prevalence of peer-to-peer operation among industrial energy hubs is considered a promoting method to enhance local energy autonomy, efficiency, and flexibility. By conducting stochastic programming, this paper aims to minimize the operating cost of multiple industrial energy hubs that trade power and heat energies with each other and utility. Concerning the important role of hydrogen systems, fuel cell-based combined heat and power (CHP) generators are employed within the hubs to increase sustainability. The presence of electric vehicles (EVs) and shiftable loads enhances the flexibility during peer-to-peer operating mode. The trading price for power and heat energies are derived at first by solving a deterministic problem and considering average values for demands and generation. However, to address the uncertainties in renewable generation, electric and heat demands, and electricity prices under operation, scenario-based stochastic programming tries to optimize the expected objective function. Furthermore, the Conditional Value-at-Risk (CVaR) approach is proposed to provide various risk-averse scheduling tactics and measure the risk of each scenario. The results show that the proposed risk-constrained approach reduces the risk and makes cautious decisions about energy resources and trades. In this regard, the expected profit has been reduced by 75%, i.e., from 1636.78 NOK under a risk-neutral case to 414.97 NOK under a risk-averse case to reduce the expected risk in operating cost.

Fuzzy Based Quick Acting DC Link Voltage Controller for Three Phases of D-Statcom Connected to the Microgrid

The microgrid systems are the combinations of solar photovoltaic, wind energy, fuel cell and battery systems, as well as conventional generators for backup. The micro grid system barriers that are encountered for their integration to the network. In power systems, voltage stability and power quality are major problems. Due to fluctuations in dc link voltage, changes in load may have an effect on the compensation. This work offers a fuzzy logic-based supervisory approach to enhance the performance of the dc link transient. The controllers employ proportional and integral gain changes with fuzzy logic PI controllers integrated with distribution static synchronous compensator (D-STATCOM). These changes occur in the transient response phase right after a load fluctuation. In order to verify the suggested controller, mathematical equations are given to compute the gains of the traditional controller using fuzzy-logic PI fast-acting dc-link voltage controllers for obtaining similar quick transient reaction. Fuzzy logic PI controller enhancements that employ greater sampling during the transient period and expert knowledge of system behavior enhance the controller's performance. Compared to a standard PI controller, a dc link error reduces the capacitor voltage during a load shift. Utilizing the suggested method and the outcomes of MATLAB simulations, it will be demonstrated that efficiency and the voltage's quick settling time.

Model coupling and comparison on optimal load shifting of battery electric vehicles and heat pumps focusing on generation adequacy

The energy transition fosters a dynamic landscape marked by renewable energy, electrification, and complex interactions among actors and technologies. Employing model experiments and comparisons shows promise for exploring these connections and enhancing model clarity and precision. This study adopts a multi-model approach, integrating a model comparison to probe how the electrification of demand-side sectors and strategic load shifts of battery electric vehicles and heat pumps might impact Germany's generation adequacy by 2030. Specific demand models from the transport and heating sectors and a future load structure projection model are interlinked with three electricity system models. The comparative analysis of the three electricity system models unveils discrepancies in dispatch decisions for power plants, flexibility options' load shifts, and their effects on generation adequacy, directly tied to model attributes.The comparison underscores methodological variations (linear optimization versus agent-based simulation, myopic foresight versus perfect foresight) as pivotal, emphasizing the significance of considering load change and start-up costs for power plants. The results show that with optimized load shifting by electric vehicles and heat pumps, the adequacy of power generation is less strained despite increased electricity demand. Moreover, load shifts mitigate curtailment of renewables and consumers, reducing carbon emissions by lowering conventional power generation.

Reactive power management in variable frequency transformer (VFT)‐integrated multi‐machine power systems: A STATCOM‐based approach

AbstractIn order to improve power system stability and suppress low power oscillations, active power regulation using variable frequency transformer (VFT) plays an important role. The literature has already documented in‐depth research on active power transfer control principles of VFT. The VFT, which is basically a wound rotor induction machine consumes reactive power because of magnetizing winding. In terms of the power flow interchange in a two‐area power system incorporating VFT, this work gives an extensive analysis to comprehend the reactive power needs under various operating circumstances. In the context of reactive power compensation, the present study explores two distinct methodologies: the implementation of a fixed shunt capacitor and the utilization of an artificial neural network (ANN)‐based static synchronous compensator (STATCOM). The STATCOM‐based control technique outperforms the conventional fixed shunt capacitor by demonstrating improved capabilities in reducing reactive power imbalances and stabilizing the power system. Further, a control scheme based on voltage regulation is implemented for integrating the STATCOM to enhance the fault ride through capability of a three‐phase bus fault. Additionally, two‐area test system is exposed to a reactive load shift to evaluate the operation of STATCOM, which restores the bus voltage to its pre‐specified value.

Wirelessly Powered and Bi-Directional Data Communication System With Adaptive Conversion Chain for Multisite Biomedical Implants Over Single Inductive Link.

A wirelessly powered and data communication system is presented which is implemented as a full system, designed for multisite implanted biomedical applications. The system is capable of receiving wireless power and data communication for each implant separately, using inductive links with different resonance frequencies. To achieve this, dual-band coils are presented in the system. In addition, the system supports bi-directional half-duplex data communication, utilizing amplitude and load shift keying (ASK and LSK) modulation schemes over a single inductive link. The system employs a digitally assisted active rectifier and an automatic resonance tuning system, to improve the power transfer efficiency (PTE) through various coupling coefficients, while minimizing the reverse current and power dissipation. The power control unit enables closed-loop monitoring to prevent high or low power delivery, and it can detect inefficient or excessive wireless power transmission or prevent temperature elevation by limiting the voltage to a safe level. A new structure of self-sampling separated- Vb ASK demodulator is proposed in the paper which is utilized within the data conversion chain, serving both the external and implanted units. The whole system is fabricated using a standard 180-nm 1.8/3.3 V CMOS process with a core area of 0.82 mm[Formula: see text]. The system is tested with coupled multisite inductive links and offers the maximum overall PTE of 31.2%, from the Tx coil to the implant load.

Demand response of large-scale residential load to concentrated variable renewable energy

Large-scale concentrated Variable Renewable Energy (VRE) sources pose a challenge to the balance and security of electricity systems. Studies have shown that the demand side may offer a greater responsiveness based on the shiftability of loads. However, there is currently no known literature on the demand response (DR) of large-scale shiftable loads in the residential sector. Firstly, there is no sufficiently large database of residential appliance-level load. Secondly, scheduling a large number of small residential shiftable loads simultaneously is a major challenge. Ultimately, the DR of large-scale residential buildings encounters the challenge of ensuring sufficient backup shiftable load to effectively cope with real-time VRE fluctuations during the response period. To address these issues, this paper proposes an appliance-level load data generation method based on diffusion neural networks. Meanwhile, a clustering method based on Generalized End to End (GE2E) loss neural networks is proposed to solve the complexity obstacles in simultaneous load swarm scheduling. Thirdly, a two-stage scheduling approach is proposed to address the challenges in the large-scale DR period through constructing day-ahead and real-time response models. The simulation results show that large-scale residential shiftable loads have a strong response capability to VRE fluctuations, and their response cost is lower than the conventional fuel-fired-power-plant-based-response within a certain user preference cost range. In practice, the clustering and scheduling methods proposed in this paper can be applied after the non-intrusive load monitoring (NILM) method is deployed to decompose the load data.