349 research outputs found
Game-theoretic modeling of curtailment rules and network investments with distributed generation
Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of sev
Strategic decision-making on low-carbon technology and network capacity investments using game theory
In recent years, renewable energy technologies have been increasingly adopted and seen as
key to humanity’s efforts to reduce greenhouse gases emissions and combat climate change.
Yet, a side effect is that renewables have reached high penetration rates in many areas,
leading to undesired curtailment, especially if existing grid infrastructure is insufficient and
renewable energy generated cannot be exported at areas of high energy demand. The issue
of curtailment is compelling at remote areas, where renewable resources are abundant,
such as in windy islands. Not only renewable production is wasted, but often curtailment
comes with high costs for renewable energy developers and energy end-users. In fact,
procedures on how generators access the grid and how curtailment is applied, are key
factors that affect the decisions of investors about generation and grid capacity installed.
Part of this thesis studies the properties of widely used curtailment rules, applied in
several countries including the UK, and their effect on strategic interactions between self-interested and profit-maximising low-carbon technology investors. The work develops a
game-theoretic framework to study the effects of curtailment on the profitability of existing
renewable projects and future developments. More specifically, work presented in this
thesis determines the upper bounds of tolerable curtailment at a given location that allows
for profitable investments. Moreover, the work studies the effect of various curtailment
strategies on the capacity factor of renewable generators and the effects of renewable
resource spatial correlation on the resulting curtailment. In fact, power network operators
face a significant knowledge gap about how to implement curtailment rules that achieve
desired operational objectives, but at the same time minimise disruption and economic
losses for renewable generators. In this context, this thesis shows that fairness and equal
sharing of imposed curtailment among generators is important to achieve maximisation of
the renewable generation capacity installed at a certain area. A new rule is proposed that
minimises disruption and the number of curtailment events a generator needs to respond
to, while achieving fair allocation of curtailment between generators of unequal ratings.
While curtailment can be reduced by smart grid techniques, a long term solution is
increasing the network capacity. Grid reinforcements, however, are expensive and costs
weight to all energy consumers. For this reason, debate in the energy community has
focused on ways to attract private investment in grid reinforcement. A key knowledge
gap faced by regulators is how to incentivise such projects, that could prove beneficial,
especially in cases where several distributed generators can use the same power line to
access the main grid, against the payment of a transmission fee. This thesis develops
methods from empirical and algorithmic game theory to provide solutions to this problem. Specifically, a two-location model is considered, where excess renewable generation
and demand are not co-located, and where a private renewable investor constructs a power
line, providing also access to other generators, against a charge for transmission. In other
words, the privately developed line is shared among all generators, a principle known
as ‘common access’ line rules. This formulation may be studied as a Stackelberg game
between transmission and local generation capacity investors. Decisions on optimal (and
interdependent) renewable capacities built by investors, affect the resulting curtailment
and profitability of projects and can be determined in the equilibrium of the game.
A first approach to study the behaviour of investors at the game equilibrium, assumed a
simple model, based on average values of renewable production and demand over a larger
time horizon. This assumption allowed for an initial examination of the Stackelberg game
equilibrium, by achieving an analytical, closed-form solution of the equilibrium and the
investigation of its properties for a wide range of cost parameters.
Next, a refined model is developed, able to capture the stochastic nature of renewable
production and variability of energy demand. A theoretical analysis of the game is
presented along with an estimation of the equilibrium by utilisation of empirical game-theoretic techniques and production/demand data from a real network upgrade project in
the UK. The proposed method is general, and can be applied to similar case studies, where
there is excess of renewable generation capacity, and where sufficient data is available.
In practice, however, available data may be erroneous or experience significant gaps.
To deal with data issues, a method for generating time series data is developed, based on
Gibbs sampling. This attains an iterative simulation analysis with different time series data
as an input (Markov Chain Monte Carlo), thus achieving the exploration of the solution
space for multiple future scenarios and leading to a reduction of the uncertainty with
regards to the investment decisions taken.
Energy storage can reduce curtailment or defer network upgrades. Hence, the last part
of this thesis proposes a model consisted of a line investor, local generators and a third
independent storage player, who can absorb renewable production, that would otherwise
have been curtailed. The model estimates optimal transmission, generation and storage capacities for various financial parameters. The value of storage is determined by comparing
the energy system operation with and without energy storage. All models proposed in this
thesis, are validated and applied to a practical setting of a grid reinforcement project, in
the UK, and a large dataset of real wind speed measurements and demand.
In summary, the research work studies the interplay among self-interested and indepen dent low-carbon investors, at areas of excess renewable capacity with network constraints
and high curtailment. The work proposes a mechanism for setting transmission charges
that ensures that the transmission line gets built, but investors from the local community,
can also benefit from investing in renewable energy and energy storage. Overall, the
results of this work show how game-theoretic techniques can help energy system stakeholders to bridge the knowledge gap about setting optimal curtailment rules and determining
appropriate transmission charges for privately developed network infrastructure.Engineering and Physical Sciences Research Council (EPSRC
Consider ethical and social challenges in smart grid research
Artificial Intelligence and Machine Learning are increasingly seen as key
technologies for building more decentralised and resilient energy grids, but
researchers must consider the ethical and social implications of their useComment: Preprint of paper published in Nature Machine Intelligence, vol. 1
(25 Nov. 2019
A review on economic and technical operation of active distribution systems
© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods
Machine Learning for Smart and Energy-Efficient Buildings
Energy consumption in buildings, both residential and commercial, accounts
for approximately 40% of all energy usage in the U.S., and similar numbers are
being reported from countries around the world. This significant amount of
energy is used to maintain a comfortable, secure, and productive environment
for the occupants. So, it is crucial that the energy consumption in buildings
must be optimized, all the while maintaining satisfactory levels of occupant
comfort, health, and safety. Recently, Machine Learning has been proven to be
an invaluable tool in deriving important insights from data and optimizing
various systems. In this work, we review the ways in which machine learning has
been leveraged to make buildings smart and energy-efficient. For the
convenience of readers, we provide a brief introduction of several machine
learning paradigms and the components and functioning of each smart building
system we cover. Finally, we discuss challenges faced while implementing
machine learning algorithms in smart buildings and provide future avenues for
research at the intersection of smart buildings and machine learning
Market design for a reliable ~100% renewable electricity system: Deliverable D3.5
Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The goal of this report is to identify in which respects the design and regulation of electricity markets needs to be improved in order facilitate a (nearly) completely decarbonized electricity system. It provides a basis for scoping the modeling analyses that are to be performed in subsequent work packages in the TradeRES project. These simulations will provide the basis for an update of this deliverable in the form of a more precise description of an all-renewable electricity market design. In this first iteration1 of deliverable 3.5, we analyze how the current design of electricity markets may fall short of future needs. Where there is a lack of certainty about the best market design choices, we identify alternative choices. Alternatives may concern a choice between policy intervention and no intervention or different intervention options. Section 2 outlines current European electricity market design and the key pieces of European legislation that underlie it. The European target model is zonal pricing with bidding zones that are defined as geographic areas within the internal market without structural congestion. That implies that within one bidding zone electricity can be traded without considering grid constraints and there are uniform wholesale prices in each zone. The main European markets are Nordpool, EPEX and MIBEL. Trading between zones in the European Price Coupling Region occurs through an implicit auction where price and quantity are computed for every hour of the next day, using EUPHEMIA, a hybrid algorithm for flowbased market coupling that is considered the best practice in Europe at this time.N/
New actor types in electricity market simulation models: Deliverable D4.4
Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The modelling of agents in the simulation models and tools is of primary importance if the quality and the validity of the simulation outcomes are at stake. This is the first version of the report that deals with the representation of electricity market actors’ in the agent based models (ABMs) used in TradeRES project. With the AMIRIS, the EMLab-Generation (EMLab), the MASCEM and the RESTrade models being in the centre of the analysis, the subject matter of this report has been the identification of the actors’ characteristics that are
already covered by the initial (with respect to the project) version of the models and the presentation of the foreseen modelling enhancements. For serving these goals, agent attributes and representation methods, as found in the literature of agent-driven models, are considered initially. The detailed review of such aspects offers the necessary background and supports the formation of a context that facilitates the mapping of actors’ characteristics to agent modelling approaches. Emphasis is given in several approaches and technics found in the literature for the development of a broader environment, on which part of the later analysis is deployed. Although the ABMs that are used in the project constitute an important part of the literature, they have not been
included in the review since they are the subject of another section.N/
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