600 research outputs found
A novel incentive-based demand response model for Cournot competition in electricity markets
This paper presents an analysis of competition between generators when
incentive-based demand response is employed in an electricity market. Thermal
and hydropower generation are considered in the model. A smooth inverse demand
function is designed using a sigmoid and two linear functions for modeling the
consumer preferences under incentive-based demand response program. Generators
compete to sell energy bilaterally to consumers and system operator provides
transmission and arbitrage services. The profit of each agent is posed as an
optimization problem, then the competition result is found by solving
simultaneously Karush-Kuhn-Tucker conditions for all generators. A Nash-Cournot
equilibrium is found when the system operates normally and at peak demand times
when DR is required. Under this model, results show that DR diminishes the
energy consumption at peak periods, shifts the power requirement to off-peak
times and improves the net consumer surplus due to incentives received for
participating in DR program. However, the generators decrease their profit due
to the reduction of traded energy and market prices
Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models
Demand Response (DR) is an opportunity and a concern for markets as well as power system flexibility. The deployment of DR depends on both knowledge on its performance and how to measure it effectively to provide adequate economic feedback. DR verification requires a baseline reference. This paper introduces a new baseline that provides an evaluation of response based on simple adjustment factors through physically-based models, tools which are also used in DR. The approach includes the detection of licit and gaming responses before and after DR. Results show that errors decrease by 10–15% with respect to conventional approaches.This work was supported by the Agencia Estatal de Investigación, Ministerio de Ciencia e Innovación (projects ENE-2016-78509-C3-2 P, RED2018-102618-T and ENE-2016-78509-C3-3P/AEI/10.13039/ 501100011033); the Ministerio de Educación (Spanish Government) under grant FPU17/02753, and EU-ERDF funds
A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism
In this paper, we propose a novel incentive based Demand Response (DR)
program with a self reported baseline mechanism. The System Operator (SO)
managing the DR program recruits consumers or aggregators of DR resources. The
recruited consumers are required to only report their baseline, which is the
minimal information necessary for any DR program. During a DR event, a set of
consumers, from this pool of recruited consumers, are randomly selected. The
consumers are selected such that the required load reduction is delivered. The
selected consumers, who reduce their load, are rewarded for their services and
other recruited consumers, who deviate from their reported baseline, are
penalized. The randomization in selection and penalty ensure that the baseline
inflation is controlled. We also justify that the selection probability can be
simultaneously used to control SO's cost. This allows the SO to design the
mechanism such that its cost is almost optimal when there are no recruitment
costs or at least significantly reduced otherwise. Finally, we also show that
the proposed method of self-reported baseline outperforms other baseline
estimation methods commonly used in practice
Mechanism Design for Demand Response Programs
Demand Response (DR) programs serve to reduce the consumption of electricity
at times when the supply is scarce and expensive. The utility informs the
aggregator of an anticipated DR event. The aggregator calls on a subset of its
pool of recruited agents to reduce their electricity use. Agents are paid for
reducing their energy consumption from contractually established baselines.
Baselines are counter-factual consumption estimates of the energy an agent
would have consumed if they were not participating in the DR program. Baselines
are used to determine payments to agents. This creates an incentive for agents
to inflate their baselines. We propose a novel self-reported baseline mechanism
(SRBM) where each agent reports its baseline and marginal utility. These
reports are strategic and need not be truthful. Based on the reported
information, the aggregator selects or calls on agents to meet the load
reduction target. Called agents are paid for observed reductions from their
self-reported baselines. Agents who are not called face penalties for
consumption shortfalls below their baselines. The mechanism is specified by the
probability with which agents are called, reward prices for called agents, and
penalty prices for agents who are not called. Under SRBM, we show that truthful
reporting of baseline consumption and marginal utility is a dominant strategy.
Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is
assured to meet the load reduction target. SRBM is also nearly efficient since
it selects agents with the smallest marginal utilities, and each called agent
contributes maximally to the load reduction target. Finally, we show that SRBM
is almost optimal in the metric of average cost of DR provision faced by the
aggregator
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
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