49,509 research outputs found
A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets
ABSTRACT: Market agents with renewable resources face amplified uncertainty when forecasting energy production to securely place bids in electricity markets. To deal with uncertainties, stochastic modelling has been applied to optimize the bidding strategy of these market agents. However, studies found in the literature usually focus on day-ahead and balancing markets, leaving aside intraday markets that could be used to correct bidding positions as uncertainty gets resolved. This paper proposes a multistage stochastic decision-aid algorithm based on linear programming to optimize the bidding strategy of market agents in three different electricity markets -day-ahead, intraday, and balance markets. The market agent represents a Virtual Power Plant with wind, solar PV, and storage technologies, and its participation in three electricity markets was compared to the participation in DA and BM markets only. Results show that participating in all three markets increased the profit achieved by the VPP agent by 10.1% while also decreasing the incurred imbalances by 63.8%. The results demonstrate that having accurate tools to deal with the multi-settlement framework of electricity markets while considering the uncertainties of daily operations is key to a successful integration of renewable energy resources into electricity markets and power systems.info:eu-repo/semantics/publishedVersio
A new approach for multi-agent coalition formation and management in the scope of electricity markets
This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself.
The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition.
In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and
analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in
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Evaluating Government's Policies on Promoting Smart Metering in Retail Electricity Markets via Agent Based Simulation
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An Agent Based Simulation of Smart Metering Technology Adoption
Based on the classic behavioural theory âthe Theory of Planned Behaviourâ, we develop an agent-based model to simulate the diffusion of smart metering technology in the electricity market. We simulate the emergent adoption of smart metering technology under different management strategies and economic regulations. Our research results show that in terms of boosting the take-off of smart meters in the electricity market, choosing the initial users on a random and geographically dispersed basis and encouraging meter competition between energy suppliers can be two very effective strategies. We also observe an âS-curveâ diffusion of smart metering technology and a âlock-inâ effect in the model. The research results provide us with insights as to effective policies and strategies for the roll-out of smart metering technology in the electricity market
Cooperatives for demand side management
We propose a new scheme for efficient demand side management for the Smart Grid. Specifically, we envisage and promote the formation of cooperatives of medium-large consumers and equip them (via our proposed mechanisms) with the capability of regularly participating in the existing electricity markets by providing electricity demand reduction services to the Grid. Based on mechanism design principles, we develop a model for such cooperatives by designing methods for estimating suitable reduction amounts, placing bids in the market and redistributing the obtained revenue amongst the member agents. Our mechanism is such that the member agents have no incentive to show artificial reductions with the aim of increasing their revenue
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
Peer-to-peer and community-based markets: A comprehensive review
The advent of more proactive consumers, the so-called "prosumers", with
production and storage capabilities, is empowering the consumers and bringing
new opportunities and challenges to the operation of power systems in a market
environment. Recently, a novel proposal for the design and operation of
electricity markets has emerged: these so-called peer-to-peer (P2P) electricity
markets conceptually allow the prosumers to directly share their electrical
energy and investment. Such P2P markets rely on a consumer-centric and
bottom-up perspective by giving the opportunity to consumers to freely choose
the way they are to source their electric energy. A community can also be
formed by prosumers who want to collaborate, or in terms of operational energy
management. This paper contributes with an overview of these new P2P markets
that starts with the motivation, challenges, market designs moving to the
potential future developments in this field, providing recommendations while
considering a test-case
A Three-Step Methodology to Improve Domestic Energy Efficiency
Increasing energy prices and the greenhouse effect lead to more awareness of energy efïŹciency of electricity supply. During the last years, a lot of technologies have been developed to improve this efïŹciency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) ofïŹine local prediction, 2) ofïŹine global planning and 3) online local scheduling. The paper ends with results of simulations and ïŹeld tests showing that the methodology is promising.\u
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