74 research outputs found
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits
The drastic growth of electric vehicles and photovoltaics can introduce new
challenges, such as electrical current congestion and voltage limit violations
due to peak load demands. These issues can be mitigated by controlling the
operation of electric vehicles i.e., smart charging. Centralized smart charging
solutions have already been proposed in the literature. But such solutions may
lack scalability and suffer from inherent drawbacks of centralization, such as
a single point of failure, and data privacy concerns. Decentralization can help
tackle these challenges. In this paper, a fully decentralized smart charging
system is proposed using the philosophy of adaptive multi-agent systems. The
proposed system utilizes multi-armed bandit learning to handle uncertainties in
the system. The presented system is decentralized, scalable, real-time,
model-free, and takes fairness among different players into account. A detailed
case study is also presented for performance evaluation.Comment: CIRED 2023 International Conference & Exhibition on Electricity
Distribution, Jun 2023, Rome, Ital
Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization
On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints
We consider the problem of an aggregator attempting to learn customers' load
flexibility models while implementing a load shaping program by means of
broadcasting daily dispatch signals. We adopt a multi-armed bandit formulation
to account for the stochastic and unknown nature of customers' responses to
dispatch signals. We propose a constrained Thompson sampling heuristic,
Con-TS-RTP, that accounts for various possible aggregator objectives (e.g., to
reduce demand at peak hours, integrate more intermittent renewable generation,
track a desired daily load profile, etc) and takes into account the operational
constraints of a distribution system to avoid potential grid failures as a
result of uncertainty in the customers' response. We provide a discussion on
the regret bounds for our algorithm as well as a discussion on the operational
reliability of the distribution system's constraints being upheld throughout
the learning process.Comment: 15 pages, IEEE Transactions on Smart Gri
Demand response performance and uncertainty: A systematic literature review
The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio
Contributions to distributed MPC: coalitional and learning approaches
A growing number of works and applications are consolidating the research area of distributed control with partial and varying communication topologies. In this context, many of the works included in this thesis focus on the so-called coalitional MPC. This approach is characterized by the dynamic formation of groups of cooperative MPC agents (referred to as coalitions) and seeks to provide a performance close to the centralized one with lighter computations and communication demands. The thesis includes a literature review of existing distributed control methods that boost scalability and flexibility by exploiting the degree of interaction between local controllers. Likewise, we present a hierarchical coalitional MPC for traffic freeways and new methods to address the agents' clustering problem, which, given its combinatoria! nature, becomes a key issue for the real-time implementation of this type of controller. Additionally, new theoretical results to provide this clustering strategy with robust and stability guarantees to track changing targets are included. Further works of this thesis focus on the application of learning techniques in distributed and decentralized MPC schemes, thus paving the way for a future extension to the coalitional framework. In this regard, we have focused on the use of neural networks to aid distributed negotiations, and on the development of a multi agent learning MPC based on a collaborative data collection
Artificial intelligence for decision making in energy demand-side response
This thesis examines the role and application of data-driven Artificial Intelligence
(AI) approaches for the energy demand-side response (DR). It follows the point of
view of a service provider company/aggregator looking to support its decision-making
and operation. Overall, the study identifies data-driven AI methods as an essential
tool and a key enabler for DR. The thesis is organised into two parts. It first provides
an overview of AI methods utilised for DR applications based on a systematic review
of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR
application area of the AI methods. The end of the first part of the thesis discusses
the advantages and potential limitations of the reviewed AI techniques for different
DR tasks and how they compare to traditional approaches. The second part of the
thesis centres around designing machine learning algorithms for DR. The undertaken
empirical work highlights the importance of data quality for providing fair, robust,
and safe AI systems in DR — a high-stakes domain. It furthers the state of the art
by providing a structured approach for data preparation and data augmentation in
DR to minimise propagating effects in the modelling process. The empirical findings
on residential response behaviour show better response behaviour in households with
internet access, air-conditioning systems, power-intensive appliances, and lower gas
usage. However, some insights raise questions about whether the reported levels of
consumers’ engagement in DR schemes translate to actual curtailment behaviour and
the individual rationale of customer response to DR signals. The presented approach
also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an
aggregator in leveraging small-scale flexibility resources by providing an automated
end-to-end framework to select the set of consumers for demand curtailment during
Demand-Side Response (DR) signals in a dynamic environment while considering a
long-term view of their selection process
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