1,123 research outputs found
Ownership Structure in Agrifood Chains
This article analyses the impact of ownership structure on investments in a three-party supply chain from an incomplete contracting perspective. Circumstances are determined in which a marketing cooperative is the unique first-best ownership structure.chains;incomplete contracts;marketing cooperatives
Cost Allocation for Inertia and Frequency Response Ancillary Services
The reduction in system inertia is creating an important market for
frequency-containment Ancillary Services (AS) such as enhanced frequency
response (e.g.,~provided by battery storage), traditional primary frequency
response and inertia itself. This market presents an important difference with
the energy-only market: while the need for energy production is driven by the
demand from consumers, frequency-containment AS are procured because of the
need to deal with the largest generation/demand loss in the system (or smaller
losses that could potentially compromise frequency stability). Thus, a question
that arises is: who should pay for frequency-containment AS?
In this work, we propose a cost-allocation methodology based on the nucleolus
concept, in order to distribute the total payments for frequency-containment AS
among all generators or loads that create the need for these services. It is
shown that this method complies with necessary properties for the AS market,
such as avoidance of cross-subsidies and maintaining players in this
cooperative game. Finally, we demonstrate its practical applicability through a
case study for the Great Britain power system, while comparing its performance
with two alternative mechanisms, namely proportional and Shapley value cost
allocation
Interpreting the Value of Flexibility in AC Security-Constrained Transmission Expansion Planning via a Cooperative Game Framework
Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review
Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) â a branch of AI â have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area
Causative Cyberattacks on Online Learning-based Automated Demand Response Systems
Power utilities are adopting Automated Demand Response (ADR) to replace the
costly fuel-fired generators and to preempt congestion during peak electricity
demand. Similarly, third-party Demand Response (DR) aggregators are leveraging
controllable small-scale electrical loads to provide on-demand grid support
services to the utilities. Some aggregators and utilities have started
employing Artificial Intelligence (AI) to learn the energy usage patterns of
electricity consumers and use this knowledge to design optimal DR incentives.
Such AI frameworks use open communication channels between the
utility/aggregator and the DR customers, which are vulnerable to
\textit{causative} data integrity cyberattacks. This paper explores
vulnerabilities of AI-based DR learning and designs a data-driven attack
strategy informed by DR data collected from the New York University (NYU)
campus buildings. The case study demonstrates the feasibility and effects of
maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent
to DR customers, and (iii) responses of DR customers to the DR incentives
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