173 research outputs found
Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach
In this paper, the problem of energy trading between smart grid prosumers,
who can simultaneously consume and produce energy, and a grid power company is
studied. The problem is formulated as a single-leader, multiple-follower
Stackelberg game between the power company and multiple prosumers. In this
game, the power company acts as a leader who determines the pricing strategy
that maximizes its profits, while the prosumers act as followers who react by
choosing the amount of energy to buy or sell so as to optimize their current
and future profits. The proposed game accounts for each prosumer's subjective
decision when faced with the uncertainty of profits, induced by the random
future price. In particular, the framing effect, from the framework of prospect
theory (PT), is used to account for each prosumer's valuation of its gains and
losses with respect to an individual utility reference point. The reference
point changes between prosumers and stems from their past experience and future
aspirations of profits. The followers' noncooperative game is shown to admit a
unique pure-strategy Nash equilibrium (NE) under classical game theory (CGT)
which is obtained using a fully distributed algorithm. The results are extended
to account for the case of PT using algorithmic solutions that can achieve an
NE under certain conditions. Simulation results show that the total grid load
varies significantly with the prosumers' reference point and their
loss-aversion level. In addition, it is shown that the power company's profits
considerably decrease when it fails to account for the prosumers' subjective
perceptions under PT
An Energy Sharing Game with Generalized Demand Bidding: Model and Properties
This paper proposes a novel energy sharing mechanism for prosumers who can
produce and consume. Different from most existing works, the role of individual
prosumer as a seller or buyer in our model is endogenously determined. Several
desirable properties of the proposed mechanism are proved based on a
generalized game-theoretic model. We show that the Nash equilibrium exists and
is the unique solution of an equivalent convex optimization problem. The
sharing price at the Nash equilibrium equals to the average marginal disutility
of all prosumers. We also prove that every prosumer has the incentive to
participate in the sharing market, and prosumers' total cost decreases with
increasing absolute value of price sensitivity. Furthermore, the Nash
equilibrium approaches the social optimal as the number of prosumers grows, and
competition can improve social welfare.Comment: 16 pages, 7 figure
Application of swarm intelligence algorithms to energy management of prosumers with wind power plants
The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers
A Classification Scheme for Local Energy Trading
The current trend towards more renewable and sustainable energy generation
leads to an increased interest in new energy management systems and the concept
of a smart grid. One important aspect of this is local energy trading, which is
an extension of existing electricity markets by including prosumers, who are
consumers also producing electricity. Prosumers having a surplus of energy may
directly trade this surplus with other prosumers, which are currently in
demand. In this paper, we present an overview of the literature in the area of
local energy trading. In order to provide structure to the broad range of
publications, we identify key characteristics, define the various settings, and
cluster the considered literature along these characteristics. We identify
three main research lines, each with a distinct setting and research question.
We analyze and compare the settings, the used techniques, and the results and
findings within each cluster and derive connections between the clusters. In
addition, we identify important aspects, which up to now have to a large extent
been neglected in the considered literature and highlight interesting research
directions, and open problems for future work.Comment: 38 pages, 1 figure, This work has been submitted and accepted at OR
Spectru
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
State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation
The competition and equilibrium in power markets under decarbonization and decentralization
Equilibrium analysis has been widely studied as an effective tool to model gaming interactions and predict market results. However, as competition modes are fundamentally changed by the decarbonization and decentralization of power systems, analysis techniques must evolve. This article comprehensively reviews recent developments in modelling methods, practical settings and solution techniques in equilibrium analysis. Firstly, we review equilibrium in the evolving wholesale power markets which feature new entrants, novel trading products and multi-stage clearing. Secondly, the competition modes in the emerging distribution market and distributed resource aggregation are reviewed, and we compare peer-to-peer clearing, cooperative games and Stackelberg games. Furthermore, we summarize the methods to treat various information acquisition degrees, risk preferences and rationalities of market participants. To deal with increasingly complex market settings, this review also covers refined analytical techniques and agent-based models used to compute the equilibrium. Finally, based on this review, this paper summarizes key issues in the gaming and equilibrium analysis in power markets under decarbonization and decentralization
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