1,235 research outputs found
Load Shifting in the Smart Grid: To Participate or Not?
Demand-side management (DSM) has emerged as an important smart grid feature
that allows utility companies to maintain desirable grid loads. However, the
success of DSM is contingent on active customer participation. Indeed, most
existing DSM studies are based on game-theoretic models that assume customers
will act rationally and will voluntarily participate in DSM. In contrast, in
this paper, the impact of customers' subjective behavior on each other's DSM
decisions is explicitly accounted for. In particular, a noncooperative game is
formulated between grid customers in which each customer can decide on whether
to participate in DSM or not. In this game, customers seek to minimize a cost
function that reflects their total payment for electricity. Unlike classical
game-theoretic DSM studies which assume that customers are rational in their
decision-making, a novel approach is proposed, based on the framework of
prospect theory (PT), to explicitly incorporate the impact of customer behavior
on DSM decisions. To solve the proposed game under both conventional game
theory and PT, a new algorithm based on fictitious player is proposed using
which the game will reach an epsilon-mixed Nash equilibrium. Simulation results
assess the impact of customer behavior on demand-side management. In
particular, the overall participation level and grid load can depend
significantly on the rationality level of the players and their risk aversion
tendency.Comment: 9 pages, 7 figures, journal, accepte
Integrating Energy Storage into the Smart Grid: A Prospect Theoretic Approach
In this paper, the interactions and energy exchange decisions of a number of
geographically distributed storage units are studied under decision-making
involving end-users. In particular, a noncooperative game is formulated between
customer-owned storage units where each storage unit's owner can decide on
whether to charge or discharge energy with a given probability so as to
maximize a utility that reflects the tradeoff between the monetary transactions
from charging/discharging and the penalty from power regulation. Unlike
existing game-theoretic works which assume that players make their decisions
rationally and objectively, we use the new framework of prospect theory (PT) to
explicitly incorporate the users' subjective perceptions of their expected
utilities. For the two-player game, we show the existence of a proper mixed
Nash equilibrium for both the standard game-theoretic case and the case with PT
considerations. Simulation results show that incorporating user behavior via PT
reveals several important insights into load management as well as economics of
energy storage usage. For instance, the results show that deviations from
conventional game theory, as predicted by PT, can lead to undesirable grid
loads and revenues thus requiring the power company to revisit its pricing
schemes and the customers to reassess their energy storage usage choices.Comment: 5 pages, 4 figures, conferenc
Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.
Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017
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/
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
Efficient double auction mechanisms in the energy grid with connected and islanded microgrids
Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSanjoy DasThe future energy grid is expected to operate in a decentralized fashion as a network of autonomous microgrids that are coordinated by a Distribution System Operator (DSO), which should allocate energy to them in an efficient manner. Each microgrid operating in either islanded or grid-connected mode may be considered to manage its own resources. This can take place through auctions with individual units of the microgrid as the agents.
This research proposes efficient auction mechanisms for the energy grid, with is-landed and connected microgrids. The microgrid level auction is carried out by means of an intermediate agent called an aggregator. The individual consumer and producer units are modeled as selfish agents. With the microgrid in islanded mode, two aggregator-level auction classes are analyzed: (i) price-heterogeneous, and (ii) price homogeneous.
Under the price heterogeneity paradigm, this research extends earlier work on the well-known, single-sided Kelly mechanism to double auctions. As in Kelly auctions, the proposed algorithm implements the bidding without using any agent level private infor-mation (i.e. generation capacity and utility functions). The proposed auction is shown to be an efficient mechanism that maximizes the social welfare, i.e. the sum of the utilities of all the agents. Furthermore, the research considers the situation where a subset of agents act as a coalition to redistribute the allocated energy and price using any other specific fairness criterion.
The price homogeneous double auction algorithm proposed in this research ad-dresses the problem of price-anticipation, where each agent tries to influence the equilibri-um price of energy by placing strategic bids. As a result of this behavior, the auction’s efficiency is lowered. This research proposes a novel approach that is implemented by the aggregator, called virtual bidding, where the efficiency can be asymptotically maximized, even in the presence of price anticipatory bidders.
Next, an auction mechanism for the energy grid, with multiple connected mi-crogrids is considered. A globally efficient bi-level auction algorithm is proposed. At the upper-level, the algorithm takes into account physical grid constraints in allocating energy to the microgrids. It is implemented by the DSO as a linear objective quadratic constraint problem that allows price heterogeneity across the aggregators. In parallel, each aggrega-tor implements its own lower-level price homogeneous auction with virtual bidding.
The research concludes with a preliminary study on extending the DSO level auc-tion to multi-period day-ahead scheduling. It takes into account storage units and conven-tional generators that are present in the grid by formulating the auction as a mixed inte-ger linear programming problem
Demand Response in Smart Grids
The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer
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