Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
DOCUMENT
This research presents a case study exploring the potential for demand side flexibility at a cluster of university buildings. The study investigates the potential of a collection of various electrical devices, excluding heating and cooling systems. With increasing penetration of renewable electricity sources and the phasing out of dispatchable fossil sources, matching grid generation with grid demand will become difficult using traditional grid management methods alone. Additionally, grid congestion is a pressing problem. Demand side management in buildings may contribute to a solution to these problems. Currently demand response is, however, not yet exploited at scale. In part, this is because it is unclear how this flexibility can be translated into successful business models, or whether this is possible under the current market regime. This research gives insight into the potential value of energy demand flexibility in reducing energy costs and increasing the match between electricity demand and purchased renewable electricity. An inventory is made of on-site electrical devices that offer load flexibility and the magnitude and duration of load shifting is estimated for each group of devices. A demand response simulation model is then developed that represents the complete collection of flexible devices. This model, addresses demand response as a ‘distribute candy’ problem and finds the optimal time-of-use for shiftable electricity demand whilst respecting the flexibility constraints of the electrical devices. The value of demand flexibility at the building cluster is then assessed using this simulation model, measured electricity consumption, and data regarding the availability of purchased renewables and day-ahead spot prices. This research concludes that coordinated demand response of large variety of devices at the building cluster level can improve energy matching by 0.6-1.5% and reduce spot market energy cost by 0.4-3.2%.
DOCUMENT
Agent-based modeling (ABM) is a widely used method for evaluating demand response (DR) strategies. To comprehensively assess the impact of DR strategies on a district cooling system, the integration of building managers’ DR behavior is essential. However, most ABM studies focus on technical optimization while overlooking the behavioral factors that may exist in building managers’ decision-making processes. To address this gap, this paper introduces an agent-based model using the belief-desire-intention (BDI) framework to simulate building managers’ air-conditioning setpoint adjustment behavior under DR, integrating the reasoning capabilities and irrational behavior factors.
MULTIFILE
Dit project heeft tot doel het ontwerp en de exploitatie van lokale energiesystemen te verbeteren voor buurten met een hoge zelfvoorziening en een hoge betrokkenheid van alle betrokken belanghebbenden. In dit project wordt een integrale aanpak toegepast door zowel technische als sociale aspecten mee te nemen.
The growing energy demand and environmental impact of traditional sources highlight the need for sustainable solutions. Hydrogen produced through water electrolysis, is a flexible and clean energy carrier capable of addressing large-electricity storage needs of the renewable but intermittent energy sources. Among various technologies, Proton Exchange Membrane Water Electrolysis (PEMWE) stands out for its efficiency and rapid response, making it ideal for grid stabilization. In its core, PEMWEs are composed of membrane electrode assemblies (MEA), which consist of a proton-conducting membrane sandwiched between two catalyst-coated electrodes, forming a single PEMWE cell unit. Despite the high efficiency and low emissions, a principal drawback of PEMWE is the capital cost due to high loading of precious metal catalysts and protective coatings. Traditional MEA catalyst coating methods are complex, inefficient, and costly to scale. To circumvent these challenges, VSParticle developed a technology for nanoparticle film production using spark ablation, which generates nanoparticles through high-voltage discharges between electrodes followed by an impaction printing module. However, the absence of liquids poses challenges, such as integrating polymeric solutions (e.g., Nafion®) for uniform, thicker catalyst coatings. Electrohydrodynamic atomization (EHDA) stands out as a promising technique thanks to its strong electric fields used to generate micro- and nanometric droplets with a narrow size distribution. Co-axial EHDA, a variation of this technique, utilizes two concentric needles to spray different fluids simultaneously.The ESPRESSO-NANO project combines co-axial EHDA with spark ablation to improve catalyst uniformity and performance at the nanometer scale by integrating electrosprayed ionomer nanoparticles with dry metal nanoparticles, ensuring better distribution of the catalyst within the nanoporous layer. This novel approach streamlines numerous steps in traditional synthesis and electrocatalyst film production which will address material waste and energy consumption, while simultaneously improve the electrochemical efficiency of PEMWEs, offering a sustainable solution to the global energy crisis.
This project aims to enhance museum experiences through value-driven design of a personalized, interactive AI-powered guide providing tailored information in real-time. Museums often rely on human guides for detailed content, but these services are typically limited to group tours. Audio guides are the common alternative for individual visitors, but they deliver standardized content that doesn't adapt to personal interests, creating a gap in the visitor experience. Additionally, creating content for traditional audio guides is expensive for museums to develop and maintain. Research shows that museum visitors increasingly desire more control over how they interact with exhibits, preferring information that aligns with their personal interests rather than generalized content. Simultaneously, museums are eager to meet this demand by offering more personalized engagement, making visitor experiences more relevant and impactful. In collaboration with Stedelijk Museum Breda and Brabant Cloud, creative professionals will co-design an app that acts as a personal digital guide, adapting its responses to each visitor's preferences. The app will use the museum's existing digital collection infrastructure to deliver customized interaction better connected to visitors' needs, interests, and linguistic abilities. The design approach emphasizes inclusivity and non-discrimination, ensuring accessible experiences for diverse audiences while aligning with the museum's educational objectives. We will test the app at Stedelijk Museum Breda, gathering feedback from three visitor groups and comparing experiences between (1) those using the AI-powered app, (2) those not using the app, and (3) those with a human guide. We anticipate that this creative industries solution will facilitate more engaging and customized experiences while providing a scalable model that could benefit other cultural institutions through Brabant Cloud's regional network.
