Distributed Energy Research Articles

Dynamic Determination Method of Line Drop Compensator Parameters for Voltage Regulators Based on Mixture of Experts Using Real‐Time Information

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Two-stage optimal PMU placement for distribution systems

The rapid growth of distributed energy resources has highlighted the importance of monitoring the system state of distribution networks. One key technology that enhances observability in distribution systems is the Phasor Measurement Unit (PMU). However, there are still challenges in effectively deploying PMUs in practice. One challenge is the solution multiplicity in optimal PMU placement (OPP). Another challenge arises from the limited deployment space available at distribution nodes, leading to incomplete observability and the need for optimization to ensure node observability. To address the challenge of solution multiplicity, this work proposes a two-stage OPP approach that considers critical node observability. The first stage optimization model determines the minimum number of PMUs required to meet observability requirements. The second stage model identifies the solution with the greatest observability redundancy while satisfying practical constraints. To validate the effectiveness of our approach, this work conducts simulations using both IEEE testing systems and a real-world distribution feeder. These simulations provide empirical evidence of the benefits of the proposed OPP model and demonstrate its applicability in practical scenarios.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Systematical analysis and application of distributed activation energy model (DAEM) with Weibull distribution for pyrolysis kinetics of lignocellulosic biomass

The distributed activation energy model (DAEM) is a comprehensive and accurate kinetic model for describing the thermal decomposition kinetics of complex materials. The Gaussian distribution is commonly used for representing the activation energy distribution, however, it is symmetric and cannot reflect the reactivity asymmetry involved in complex chemical kinetics. The Weibull distribution offers an alternative to represent the distribution of activation energies. This study performed a systematical investigation of the DAEM with Weibull distribution, focusing particularly on its asymmetric characteristics. Additionally, the DAEM with Weibull distribution was applied to analyze the pyrolysis kinetics of rice straw and cotton stalk. The theoretical analysis results showed that the DAEM with Weibull distribution could provide abundant flexibility to elucidate the asymmetric reactivity of complex chemical kinetics. The pyrolysis processes of rice straw and cotton stalk could be deconvoluted into several sub-processes, which could be accurately described by the DAEMs with Weibull distribution. The obtained Weibull distributions of activation energies for those deconvoluted sub-processes validated their asymmetric reactivities. It is expected that the results could help in exploring the kinetic mechanisms of lignocellulosic biomass pyrolysis and facilitate the application of the DAEM with Weibull distribution in analyzing complex chemical kinetics.

Lyapunov-based distributed secondary frequency and voltage control for distributed energy resources in islanded microgrids with expected dynamic performance improvement

Microgrids (MGs) can effectively integrate the high penetration distributed energy resources (DERs) for the energy and environmental crisis. Still, fluctuations in intermittent DERs may lead to severe frequency and voltage deviations or even the instability of islanded MGs. Secondary control has successfully compensated for the frequency and voltage deviations. Research on secondary control mainly focused on steady-state operating objectives, i.e., frequency and voltage recovery and power sharing. Still, improving the dynamic responses of secondary control is crucial for system-stable operation, especially in the presence of synchronous DERs. This is because these units can cause undesired oscillatory modes, leading to system instability. In addition, because of the line impedance effect, accurate reactive power sharing is usually ignored when voltage recovery is considered. This will lead to an overload of MGs. To improve the dynamics of secondary control while trading off voltage regulation and reactive power sharing, we propose a Lyapunov-Function (LF)–based distributed secondary control strategy. In the proposed LF-based control strategy, the frequency, voltage, and power information are introduced into feedback control based on the Lyapunov stability theorem. Therefore, the improved frequency and voltage deviations and accurate power sharing can be guaranteed when the Lyapunov function asymptotically attenuates to zero. The inherent conflict between voltage regulation and reactive power sharing is addressed by converging the average voltage to the rated reference. Besides, the improved dynamic performance of secondary control is achieved by considering global power variations, which are obtained by distribution-level phasor measurement units. Global power variations can establish control actions to dampen system oscillations and accelerate system restoration. Furthermore, the controller design method based on the Lyapunov stability theorem can naturally promise the stability of the proposed secondary frequency and voltage controllers. Numerical simulations on the IEEE 34-bus system validate that the proposed control strategy can ensure the significantly improved dynamic performance of secondary control while achieving steady-state operation objectives.

Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?

The integration of AI and ML into energy forecasting is crucial for modern energy management. Federated Learning (FL) is particularly noteworthy because it enhances data privacy and facilitates collaboration among distributed energy resources. It enables model training across multiple locations while minimizing reliance on centralized servers and data transfers. However, FL faces significant security challenges, particularly from adversarial attacks that can undermine the models' integrity and reliability. This paper addresses these security concerns by evaluating the effectiveness of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) in distributed load forecasting. We conducted a comparative analysis using two publicly available datasets, household data and smart grid data, for short-term load forecasting. Our study finds that DFL is more robust against adversarial attacks and artificial noise compared to CFL. Specifically, adversarial model poisoning attacks impact only the targeted client in DFL, while CFL is more broadly affected. For instance, in the presence of attacks, CFL's average client Mean Absolute Error (MAE) increased from 0.084 to 0.78 kWh for Dataset 1 and from 0.475 to 11.159 MW for Dataset 2, whereas DFL maintained relatively low error rates. Conversely, artificial noise had a less pronounced effect on CFL compared to DFL. Additionally, we introduce Decentralized Random Layer Aggregation (DRLA) to enhance DFL's robustness, offering further insights into improving FL methodologies in the energy sector. Our findings also indicate that only a single layer of the neural network is required for local updates during the aggregation process in DFL.

Scheduling and assessment of multi-area virtual power plant including flexible resources using swarm intelligence technique

A virtual power plant (VPP) makes the distributed energy resources (DERs) visible to support grid services, promoting clean energy use by offering an alternative to reduce reliance on conventional sources. This paper proposes optimized operation and scheduling of VPP by forming a group of flexible resources that includes renewables i.e., solar, wind power, fuel cell, and co-generation units along with storage facilities to form a holistic network in a multi-area interconnected system framework. Initially, the scheduling is performed for a single area system in which a comparison is made with the available work relevant to the proposed formulation. In addition, the scheduling is further extended to the proposed multi-area based virtual power plant (MAVPP) performed on a day-ahead (DA) basis and the target objective(s) i.e., net profit and emission are optimized. Furthermore, a recently developed marine predator optimization algorithm (MPOA) is employed to solve the proposed problem formulation and the results are compared and discussed extensively with conventional techniques. Also, the analysis pertaining to MAVPP is carried out which is beneficial in terms of economic and environmental perspective. The result reveals that the maximum value of net profit for MAVPP is converging to an optimum value of 59,351.88 $ followed by the attained value of minimum emission of 51,700.73 Kg by consuming an elapsed time of 92.96 and 104.76 s., respectively in 200 iterations. The outcomes are promising and upon comparison with the available literature work, the superiority of the selected technique is evident in terms of computational time, improvement of net profit and reduction in emission followed by the ease of convergence behaviour in fewer iterations.

System friendliness of distributed resources in sustainable energy systems

The increasing share of volatile renewable energy generation in current and future energy systems which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term “system friendliness” which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of “system friendliness” and develop comprehensive and holistic related indicators. For that, we introduce the burden concept for energy systems which represents the amount of technical infrastructure required for stable operation. We use the concept of a hypothetical system energy storage to derive indicators that are capable of quantifying the system friendliness of a given point-of-interest. The developed indicators are independent of regulations or market environments which enables comparability of results between studies and different systems. In a case study we demonstrate our newly developed methodology and exemplary examine decentral district energy storages. Our findings demonstrate that today’s regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.

Forecasting masked‐load with invisible distributed energy resources based on transfer learning and Bayesian tuning

AbstractLoad forecasting with distributed energy resources (DERs) behind‐the‐meter is more challenging owing to transformed data patterns. Traditional forecasting method which is only based on unmasked‐load could not suit the present limited masked‐load. To bridge the divergence between unmasked‐load and masked‐load, this article proposes a masked‐load forecasting (MLF) method based on transfer learning technique and Bayesian optimization, which is Maximum Mean Discrepancy‐Neural Network with Bayesian optimization (MMD‐NNb). At first, common feature vectors between unmasked‐load and masked‐load are extracted and an outcome predictor could be established based on feature vectors from historical unmasked‐load. The feature vectors from masked‐load could therefore accommodate to the outcome predictor, and the masked‐load could be forecast. Owing to the excessive hyperparameters involved in training, Bayesian optimization is adopted for hyperparameters fine‐tuning. MMD‐NNb was tested and compared with four related models. The improvements from MMD‐NNb were observed in all comparison scenarios. Also, MMD‐NNb was proved to have high resilience to the different DERs and not requiring additional DERs‐data.

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

Large-scale energy storage for carbon neutrality: thermal energy storage for electrical vehicles

AbstractThermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

The Evolution of the Land Drilling Rig

_ The earliest land rigs used materials like bamboo and iron and were powered by animals such as horses or oxen. These rigs used percussive drilling to create the bore, repeatedly raising and dropping heavy iron bits—effective but slow. By the time Edwin Drake was drilling the Titusville well in 1859, steam engines had been introduced to help power now metal pipe down into the Earth’s crust. By the 1950s, steam-powered rigs gave way to diesel engines. The 1960s would see the advent of self-elevating rigs—the transition from derricks to mast. During the 1970s, electrical power using on-site generators was introduced to drive systems on the rig, and the advent of new downhole tools would allow drillers to directionally drill while maintaining rotation. In the mid-1980s, the first steerable drilling system was introduced. Soon, the evolution would move from the drill floor and downhole into the doghouse (driller’s cabin). Gone were the mechanical hand brakes and foot pedal systems, replaced by touchscreens and joysticks. The land rig has come a long way. Hoisting capability and rig-up requirements were key drivers in the design of older units rather than an ability to store materials and move. Older rig-floor configurations were modified to accommodate pipe-handling equipment instead of designed to integrate pipe-handling equipment. These units were not driven by location sizes nor multiwell pad capabilities. Most of this would change with the coming of the new millennia, and by 2010, it would seem like new innovations would be integrated into land rig design every year—from larger variable skid systems allowing for pad and batch drilling to app-based automation driving efficiency and safety profile. Topdrives? Onshore? Most of what drove innovation in land rigs can be traced back to development in the offshore sector. Much of what would become standard technology on land had its roots offshore, starting with one of the most important technology transfers of the 1990s—topdrives.

Experimental study and techno-economic evaluation of an active fault detection kit in the prospect of future zero energy building installations

This article presents an active fault detection kit, applicable to low voltage single-phase electrical installations, in the prospect of the Zero Energy Building concept, integrating on-site renewable energy generation and energy storage. This simple, flexible, self-powered and compact kit is capable of detecting faults, such as power theft (meter tampering), unintentional islanding and neutral conductor loss. Its operation is based on harmonic voltage injection, in series with the electrical installation, through a low-power H-bridge inverter and a current transformer, along with the corresponding harmonic current measurement, to estimate the impedance and effectively detect faulty conditions; the fast and robust Goertzel algorithm is utilized. Moreover, it features IoT communication capabilities, employing the ESP32 microcontroller, to exchange data and information with the installation meters. The functionality and effective fault detection of the proposed device are validated, through experimental tests on a custom-developed hardware prototype; IoT connectivity and data uploading are experimentally tested and verified, too. Finally, a sustainability assessment study is performed, using the Life Cycle Cost Analysis tool.

Current Progress of EUV Spectral Responsivity Calibration for EUV Lithography at CMS/ITRI

Abstract To support the development of advanced EUV lithography for the semiconductor industry in Taiwan, a calibration system for photodiodes’ spectral responsivity was established. The current system utilizes the synchrotron radiation light source and the method is traceable to PTB, Germany. The wavelength range is from 10 nm to 15 nm, including the most often used 13.5 nm. Several techniques were studied to compensate for light source fluctuation and to reduce the measurement uncertainty. The relative expanded uncertainty of the spectral responsivity calibration at 13.5 nm is 4.6 % (k=2). A wafer-type EUV radiant power meter designed to be used in EUV lithography chambers is being developed. Our goal is to develop simple and reliable methods for on-site EUV optical power measurement and dose estimation.

Empowering energy management in smart buildings: A comprehensive study on distributed energy storage systems for Sustainable consumption

The increment of photovoltaic generation in smart buildings and energy communities makes the use of energy storage systems desired to increase the self-consumption efficiency. This paper proposes and explores a model for energy storage systems management that considers local renewable generation, local demand, and retailer energy prices. The proposed model was tested at both energy community-level and the smart building level, demonstrating their capabilities of deployment. To validate the proposed model, a case study with two scenarios, including a 251 members energy community, was executed. The results demonstrate significant cost reductions for community members when adopting energy storage systems and the proposed management model. Regarding the smart building application four scenarios were tested, it is demonstrated that the demand for energy from the retailer could be set to zero during periods of time to enable its participation in demand response events. Overall, this paper contributes to the state-of-the-art by identifying and evaluating a model that manages the energy storage systems charge and discharge operation to actively reduce energy costs at the community-level (19.26%) and building level (11.75%) and to demonstrate that part of the loads can be optimized to understand if the building can be energy net-zero.

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures

Compared to centralized generation technology, distributed energy resource systems are susceptible to energy risks caused by boundary uncertainties and unit failures. This study introduces a stochastic two-stage multi-objective optimization method to address reliability-based unit commitment issues. In the day-ahead stage, operational state and reserve capacity are determined to minimize prescheduled operation costs based on forecasted parameters. In the real-time stage, a decision-dependent stochastic reliability method is proposed to simulate outage scenarios. Reserve resources within available units are allocated to mitigate forecasting errors and unit failures. Additionally, the grid interaction ratio and penalty cost are added to restrict the depth and frequency access to the grid. Four comparative cases analyze the effects of the proposed methodology. This method innovatively achieves the simulation of stochastic multi-unit outages and delete faulty units in the operation scheme. The optimal results show that the risks of electricity and cooling supply are underestimated, while the risks of heating are overestimated, compared to N-1 reliability. Furthermore, Pareto analysis of the multi-objective problem enhances independent operational capacity through utilization of reserve resources. Grid dispatch pressure is reduced since purchased power can be used as day-ahead planning. Thus, the methodology achieves collaborative optimization of reliability with a reduction of operation costs, offering effective guidance for engineering applications.