Flexible Demand Research Articles

Enhanced SWIR Light Detection in Organic Semiconductor Photodetectors through Up-Conversion of Mid-Gap Trap States.

Shortwave-infrared (SWIR) photodetectors are vital for many scientific and industrial applications including surveillance, quality control and inspection. In recent decades, photodetectors based on organic semiconductors have emerged, demonstrating potential to add real value to broadband and narrowband imaging and sensing scenarios, where factors such as thermal budget sensitivity, large area aperture necessity, cost considerations, and lightweight and conformal flexibility demands are prioritized. It is now recognized that the performance of organic photodetectors (OPDs), notably their specific detectivity, is ultimately limited by trap states, universally present in disordered semiconductors. This work adopts an approach of utilizing these mid-gap states to specifically create a SWIR photo-response. To this end, this work introduces a somewhat counter-intuitive approach of "trap-doping" in bulk heterojunction (BHJs) photodiodes, where small quantities of a guest organic molecule are intentionally incorporated into a semiconducting donor:acceptor host system. Following this approach, this work demonstrates a proof-of-concept for a visible-to-SWIR broadband OPD, approaching (and, to some extent, even exceeding) state-of-the-art performance across critical photodetector metrics. The trap-doping approach is, even though only a proof-of-concept currently, broadly applicable to various spectral windows. It represents a new modality for engineering photodetection using the unconventional strategy of turning a limitation into a feature.

Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Design, computational analysis and experimental study of a high amplification piezoelectric actuated microgripper

Increasing applications of compliant microgripper demands flexibility in working with a wide range of micro-objects which requires a large workspace, high precision motion, low parasitic motion, and satisfactory bandwidth control. To meet the requirement of pick and place manipulation tasks, a high amplification piezoelectric actuated microgripper is proposed and investigated in this paper. The high amplification of the microgripper is achieved using a compound amplifier. The compound amplifier is assisted to magnify the embedded piezoelectric actuator’s displacement. Two cascaded lever-type mechanisms are symmetrically connected with a bridge-type mechanism and form a three-stage amplification mechanism-based compound amplifier. Further, the four-bar parallelogram mechanisms are integrated with the third-stage displacement amplification mechanisms to linearize the output motion of the microgripper jaws. The characteristics of the microgripper were evaluated by computational analysis and validated using experimental investigations. Further, the design parameters are identified from the geometrical model of the individual displacement transmission mechanisms to perform a response surface optimization on the configured mechanism by the computational method. The design optimization of the microgripper resulted in a high displacement amplification ratio with a large workspace. The experimental investigations show that the designed microgripper is capable of achieving a high displacement amplification ratio of 34.5 and a total output displacement of 529.4 μm. Further, the characteristics of the microgripper such as motion resolution, and parasitic motion indicate that it will be able to perform high-precision micro-object grasping/releasing tasks.

A constrained price-based demand response framework employing utility functions in three-state Overlapping Generation and Gift and Bequest based model in distribution system

Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. These programs are important as they have the potential to help electricity providers save money through reductions in peak demand and the ability to defer construction of new power plants and power delivery systems specifically, those reserved for use during peak times. For the successful application of DR in day-to-day life, DR models are necessary to be implemented. Many of the existing DR models primarily focus on the formulation of after-DR demand based on price elasticity. Though these models are devoid of basic humans’ micro-economic behavior, which is an essential part of a DR stakeholder. Considering these shortcomings of the existing DR literature, this paper envisages formulating DR models based on the foundation of basic humans’ manifestations of demand flexibility, willingness, load recovery, and altruistic behavior. Hence, this paper proposes two price-based DR models known as the three-state Overlapping Generation (OLG) model and the Gift and Bequest (G&B) based DR model. These models are based on customers’ microeconomic behaviors and are suitable for representing load recovery with minimal parameters. Both three-state OLG and G&B-based DR models are examined on IEEE 33-bus and 118-bus distribution systems and are compared with the existing price-elasticity model (PEM) and two-state OLG-based DR model.

Optimal design of greenfield energy hubs in the context of carbon neutral energy supply

This paper proposes a sequential co-optimization (SCO) framework for the design of greenfield energy hubs, aiming to achieve net zero emissions. The SCO framework offers an evolutionary approach to long-term planning and operation, taking into account various evolving factors such as carbon emission policies, technological and economic advancements in energy supply components, changes in land-use, and developments at different stages of the design horizon. The framework ensures that the design of greenfield developments aligns with their evolving characteristics, determining the optimal dispatch flow for firming renewable energy through co-generation and demand flexibility options like smart loads, electric vehicles, and hydrogen-based systems. A sensitivity analysis is conducted to study the impact of emissions, evolving characteristics of greenfield developments, and techno-economic aspects of supply units on the energy hub design. Case studies demonstrate that the proposed design enables large-scale distributed energy systems to meet local energy demand and participate in the electricity market. The SCO framework provides a robust and flexible platform for the correlated optimization of planning and operation of greenfield energy hubs, for advancing towards net zero emissions.

Decision-focused learning under decision dependent uncertainty for power systems with price-responsive demand

Contemporary power systems are experiencing a growing integration of flexible resources on the consumer side. Different from flexible demand that submits specific bids to energy markets, price-responsive demand (PRD) adjusts its power consumption without notice, simply based on the resulting electricity prices as well as internal priorities and limitations. In this paper, we demonstrate how a high penetration of PRD (the behavior of which is invisible to the operator during the day-ahead unit commitment stage) results in systematic inefficiency costs and formulate the so-termed decision-focused learning problem of learning to provide a demand forecast which, once fed as an input to the operator’s economic dispatch optimization problem, results in an efficient dispatch. Interestingly, the prescribed demand forecast affects the resulting prices, which in turn affect the actual demand realization, giving rise to a decision-dependent uncertainty. Motivated by the problem’s hard-to-evaluate objective function, we solve it using Bayesian optimization. The empirical evaluations demonstrate significant savings in the effective real-time system cost, compared to the current practice of using the default demand forecast. Moreover, the method is shown to achieve a system cost that is fairly close to the one achieved by a system that fully integrates PRD into the day-ahead process; but without requiring any change in the operator’s existing dispatch algorithm while avoiding all efforts necessary for the integration of flexible demand, which is a widely pursued field of ongoing research.

Virtual Power Plant Scheduling Model Considering the Power Consumption Satisfaction of Users with Different Flexible Loads

Abstract Aiming at the problem that customer satisfaction with electricity consumption will affect the implementation effect of demand response. This paper proposes a virtual power plant scheduling model considering the satisfaction of users with different flexible loads. Firstly, the flexible load is divided into three categories: reducible load, transferable load and substitutable load. By analyzing the factors that affect the satisfaction of electricity consumption, the satisfaction models of customers with different flexible loads are constructed respectively. Secondly, a virtual power plant scheduling model considering different flexible load demand response models is established. Finally, the scheduling model is simulated and solved based on MATLAB, and the validity of the proposed virtual power plant scheduling model considering the satisfaction of users with different flexible loads is verified.

The price signal paradigm – On the evolution and limitations of demand-side flexibility in the EU

Demand-side flexibility (DSF) generally refers to adjusting electricity demand to better match supply and capacity in the grid. Widely seen as a central pillar of the energy transition, it is anticipated to play a key role in decarbonization. Despite its recent popularity, its efficacy as a policy instrument remains limited. This paper argues that DSF is being hindered by an overreliance on what is seen as its primary lever – the price signal. The price signal “paradigm” refers to how the general problem of fostering a more flexible demand side has been constrained to the narrow task of having users respond to price signals. The consequence of this development has been for policymakers and system operators to limit themselves to the role of information providers and neutral market facilitators, absolving themselves of more direct obligations and shifting the bulk of the responsibility onto end users who are required to actively participate in the electricity market. This paper explores the emergence of this paradigm, shedding light on the historical factors that have shaped the evolution of DSF, and the limitations inherent to its contemporary form. It outlines policy recommendations and an alternative approach that centers on shifting more responsibility towards system operators.

Empowered or enchained? Exploring consumer perspectives on Direct Load Control

Demand Response has become a central focus in policy discussions, attracting heightened attention for its potential key role in fostering enhanced demand flexibility. Direct Load Control (DLC) holds particular promise, requiring reduced consumer involvement compared to strategies relying on manual responses to time-varying pricing. However, the participation rate of residential consumers in DLC programs remains low, emphasizing the need for a more profound understanding of consumers' perspectives on DLC. Drawing from 15 in-depth interviews with Swedish households participating in a program involving direct load control of heat pumps, this study aims to explore the experiences and perceptions of consumers, providing in-depth understanding on factors that may serve as motivators, barriers and enablers for participation. Key findings include: financial benefits and interest in technology were the main motivations for participation; pre-existing knowledge and awareness of energy-related matters shaped consumers’ attitudes to DLC; trust in the service provider was a key enabling factor for participation. The study further suggests that DLC does not inevitably lead to a perceived loss of control among participants, but, if implemented in alignment with their specific conditions, needs and preferences, may also foster a sense of empowerment.

Development of flexible multi-phase barium titanate piezoelectric sensor for physiological health and action behavior monitoring

Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 10 4 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 10 4 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.

Investigating intentions and barriers in adopting decentralized home energy management systems: A justice dimension of demand flexibility

The integration of a decentralized home energy management system (HEMS) marks a pivotal advancement in the pursuit of enhanced energy efficiency and sustainability within modern households. While numerous studies focus on developing efficient and innovative programs from technical perspectives, the willingness of individuals to adopt these systems is equally crucial for achieving widespread adoption and ultimately creating a more energy-efficient society. Based on a large-scale online survey with 1,196 participants in California in 2021, we investigated the intentions related to the adoption of decentralized HEMS, particularly considering demand flexibility encompassing both air-conditioning (AC) and electric vehicles (EV) control, specifically focusing on socio-demographic disparities. Our analysis found greater openness for allowing HEMS to control AC usage compared to scheduling EV charging, possibly due to immediate comfort needs or trust in AC predictability. Low-income households showed less flexibility in adjusting both AC and EVs, while high-income households were less likely to decrease EV charging. Furthermore, homeowners exhibited greater flexibility compared to renters. Disparities between different racial backgrounds in EV charging time-shifting were more pronounced than in AC aspects. Our findings indicated that vulnerable populations may lack the flexibility and resources necessary to shift their energy consumption patterns effectively, potentially amplifying energy-related disparities and exacerbating their energy burden. Policy recommendations highlight the need for multifaceted approaches in addressing demand flexibility and energy management, especially with emerging technologies like EVs, to ensure equitable strategies for promoting sustainable energy practices across diverse communities.

Flexible copper: Exploring capacity-based energy demand flexibility in the industry

Different forms of flexibility can help in balancing variable generation. This work focuses on industrial demand-side flexibility applied in copper production, which is expected to grow for the build-out of green technologies.This study assesses the potential of capacity-based energy demand flexibility (over-sizing production processes) in an industry embedded in fully renewable energy systems. For this, an optimization model for multi-vector energy systems planning is extended so that it also includes the sizing and operation of a production process. A case study is presented for copper production, with greenfield results until 2050.Results show that flexibility at the concentration and refining stages belongs to the cost-optimal system design, at least over the next decade. At current costs, the potential cost savings in the energy system for the production process through capacity-based demand flexibility range from 5 % to 12 %, depending on the technology scenario. These potential savings are expected to decrease over time if cost reductions of renewable energy supply and storage technologies materialize. Technology scenarios considering seawater pumped-hydro energy storage yield lower costs over the entire projected period.

Research on distribution–microgrid-coupled network demand response based on a multi-time scale

Under the background of “dual carbon” strategy, the integration of renewable energy adds volatility to the grid. Relying solely on generation-side resources for regulation is inadequate, necessitating a flexible demand response from diverse demandside resources. This paper employs a physical connection and information exchange between the distribution network and microgrids to leverage the advantages of centralized-distributed optimization. This establishes a coordinated demand response model between the distribution network and microgrids, gradually establishing a new type of distribution network that integrates interconnected grids and microgrids. This also necessitates the analysis of the response characteristics of various load resources within microgrids and the categorization and modeling of loads based on their response speeds. Additionally, a method for evaluating the multi-time scale schedulable capacity of microgrids is proposed. Finally, a coordinated demand response model between the distribution network and microgrids based on the schedulable capacity assessment is established. This model is validated through case studies, demonstrating its effectiveness. The coordinated demand response between distribution networks and microgrids enables them to operate in a collaborative and economically safe manner.

The new role of sustainable hydropower in flexible energy systems and its technical evolution through innovation and digitalization

Hydropower has played an important role in Europe in recent decades, offering a unique combination of safe, low-cost, and clean power generation. Today, it is still one of the largest renewable energy sources (RES), accounting for about 35 % of RES electricity generation. However, grid stability is threatened by the increasing amount of undispatchable RES. Flexibility and dynamics such as energy storage and rapid response are urgently needed to achieve EU policy goals. In such a context, hydropower can play a key role, not only as a provider of regulated renewable energy but also due to its ability to balance a renewable energy system in the short term and the medium/long term by pumped storage technology. All these aspects underline the new role of hydropower, which aims to strengthen grid stability and power supply resilience and to enable higher penetration of volatile RES. In this context, the aim of this paper is to demonstrate the role of hydropower at the European level as well as the needs and opportunities of modernization to fully exploit its potential. In particular, this study provides, the assessment of the today flexibility offered by hydropower to the power system at the European level by leveraging a database of information collected through the participants to the working groups of the COST Action Pen@Hydropower which includes stakeholders in the hydropower sector of 34 European countries. The study confirms the key role of hydropower in future energy scenarios with 30 % of the flexibility demand at all time scales met by hydropower. Furthermore, the paper presents a review of the digitalization solutions and innovative technologies that support the growth of a new generation of sustainable hydropower together with the modernization opportunities for existing hydropower plants. The results of this work have practical implications for stakeholders in the hydropower sector and policymakers as it provides evidence at the European scale of the role of hydropower technology in balancing the power system and the need to have supportive frameworks and adequate markets to fully exploit the European hydropower potential to achieve the energy transition goals.

Control-oriented modeling of direct-heat co-current rotary dryers for energy demand flexibility

The contribution presents a new control-oriented model for direct-heat co-current rotary dryers with a focus on demand-side flexibility considering the variation of material throughput and heat source. The dryer model is based on partial differential-algebraic equations covering time- and location-dependent gas and particle velocities taking phase interactions into account. In addition, a steady-state model of a heat supply unit covering electricity-based and chemical heat sources is presented. The model is qualitatively compared with measured data and models from literature showing good overall agreement. Furthermore, simulations of the flexibility mechanisms are performed and their effect on temperature and humidity profiles as well as input and output streams of the dryer are analyzed. On this basis, requirements to process control are discussed. The simulations show that the variation of the throughput is associated with significant changes to the gas input stream, while only small changes are observed for the variation of the energy carrier.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

Enhancing Resource Allocation for Multi-Energy Storage Systems: A Comprehensive Approach Considering Supply and Demand Flexibility and Integration of New Energy

This study presents an innovative optimization method for resource scheduling in multi-energy storage systems, focusing on improving resource allocation while considering supply-demand flexibility and renewable energy integration. As renewable energy gains popularity and multi-energy systems become more complex, effective utilization of energy storage to achieve supply-demand balance, optimize energy scheduling, and maximize renewable energy integration is crucial. To address this challenge, a Markov dynamic model is developed to capture the dynamic changes in energy supply and demand within the multi-energy storage system. The model is then solved using a reinforcement learning approach to optimize resource scheduling decisions. Numerical simulations and case studies are conducted to validate the effectiveness and feasibility of the proposed method, showcasing its potential to enhance operational efficiency and reliability in multi-energy storage systems amidst constantly changing energy patterns. This research provides valuable insights and decision support for the design and operation of multi-energy storage systems, contributing to the advancement of sustainable energy utilization and promoting sustainable development in the energy sector.

Demand for Commitment in Credit and Saving Contracts: A Field Experiment

Abstract We conduct a field experiment in which we offer credit and saving contracts to the same pool of Pakistani microfinance clients. Additional treatments test ex ante demand for soft commitment (in the form of reminders, either to respondents or to their families), hard commitment (in the form of a penalty for missing an instalment) and flexibility (an option to postpone an instalment) to save or pay loan instalments on time. We find substantial demand for fixed repayment contracts in both the credit and savings domains, in ways that imply that respondents value the commitment required. While we find little or no average demand for additional contractual features, we nonetheless observe that different combinations of contractual add-ons are preferred depending on the respondent’s level of financial discipline. Respondents with high financial discipline prefer flexibility in credit contracts when combined with reminders to self while those with low discipline value penalties in savings contracts only when paired with reminders. Our results imply that, for the average microfinance client, demand for commitment is met through the regular payment schedule built into standard microcredit or commitment savings contracts. However, combining penalties or flexibility with reminders may appeal to certain subsets of clients.

Flexible resource demand response scheduling strategy under 5G-V2X

Electric vehicles (EV) and 5 G base stations, functioning as flexible load resources, contribute significantly to the provision of ample reserve capacity for the power grid. The emergence of 5 G Vehicle-to-Everything (V2X) technology has fostered a coupling between EV and base stations, further enhancing their roles within the power grid. Building upon this foundation, this paper proposes a scheduling strategy for demand response in flexibility resources under the 5 G-V2X framework. Initially, the dispatchable load of EV is examined in conjunction with the traffic network. Subsequently, we consider the communication power consumption resulting from the connected vehicle model, enabling us to devise path planning and charging navigation strategies for EV. Lastly, we establish a scheduling decision model that aims to minimize both the operation costs of base stations and the variance in grid load. The simulation is validated using real-world road conditions in the primary urban area of a city. The findings illustrate that the proposed dispatching strategy not only achieves a mutually beneficial outcome for EV users and aggregators, without disrupting EV charging and discharging processes, but also enhances the capability of demand response, thereby ensuring reliable grid operation.

The flexible reverse approach for decomposing economic inefficiency: With an application to Taiwanese banks

Profit inefficiency is conventionally decomposed into two mutually exclusive components representing profit loss due to technical inefficiency, and, through duality theory, a residual interpreted as allocative inefficiency. Although conventional models solve technical inefficiencies by reducing inputs and increasing outputs, achieving profit efficiency may require larger than observed input quantities and/or smaller than observed output quantities. However, overcoming the restrictions in the direction of the technical adjustments in input and output quantities demands flexibility that existing models do not offer. Thus, to achieve this flexibility, we introduce an endogenous profit inefficiency measure that reverses the subordinate role played by allocative inefficiency. The new measure is based on a monetized version of the weighted additive model seeking maximum feasible profit gains without restricting inputs and output adjustments. This prevents the conflicting prescriptions that the conventional model may offer in the form of non-monotonic input and output changes, thereby reducing adjustment costs. We apply the proposed model to real data from financial institutions. The differences in the managerial and policy recommendations for optimal resource allocation are relevant, with the conventional model wrongly recommending reductions in inputs in terms of the amounts and scale required to maximize profit.