4,388 research outputs found
Capturing Aggregate Flexibility in Demand Response
Flexibility in electric power consumption can be leveraged by Demand Response
(DR) programs. The goal of this paper is to systematically capture the inherent
aggregate flexibility of a population of appliances. We do so by clustering
individual loads based on their characteristics and service constraints. We
highlight the challenges associated with learning the customer response to
economic incentives while applying demand side management to heterogeneous
appliances. We also develop a framework to quantify customer privacy in direct
load scheduling programs.Comment: Submitted to IEEE CDC 201
Co-Simulation of Electric Power Distribution and Buildings with EnergyPlus and OpenDSS
Problem Formulation Need for accurate load modeling that incorporates human comfort into community-level demand response (DR) studies Achievable through co-simulation of advanced digital twins for buildings with EnergyPlus and distribution systems with OpenDS
Contract Design for Energy Demand Response
Power companies such as Southern California Edison (SCE) uses Demand Response
(DR) contracts to incentivize consumers to reduce their power consumption
during periods when demand forecast exceeds supply. Current mechanisms in use
offer contracts to consumers independent of one another, do not take into
consideration consumers' heterogeneity in consumption profile or reliability,
and fail to achieve high participation.
We introduce DR-VCG, a new DR mechanism that offers a flexible set of
contracts (which may include the standard SCE contracts) and uses VCG pricing.
We prove that DR-VCG elicits truthful bids, incentivizes honest preparation
efforts, enables efficient computation of allocation and prices. With simple
fixed-penalty contracts, the optimization goal of the mechanism is an upper
bound on probability that the reduction target is missed. Extensive simulations
show that compared to the current mechanism deployed in by SCE, the DR-VCG
mechanism achieves higher participation, increased reliability, and
significantly reduced total expenses.Comment: full version of paper accepted to IJCAI'1
The role of demand response in mitigating market power - A quantitative analysis using a stochastic market equilibrium model. ESRI WP635, August 2019
Market power is a dominant feature of many modern electricity markets with an oligopolistic structure, resulting in
increased consumer cost. This work investigates how consumers, through demand response (DR), can mitigate against market
power. Within DR, our analysis particularly focusses on the impacts of load shifting and self-generation. A stochastic mixed
complementarity problem is presented to model an electricity market characterised by oligopoly with a competitive fringe. It
incorporates both energy and capacity markets, multiple generating firms and different consumer types. The model is applied to
a case study based on data for the Irish power system in 2025. The results demonstrate how DR can help consumers mitigate
against the negative effects of market power and that load shifting and self-generation are competing technologies, whose
effectivity against market power is similar for most consumers. We also find that DR does not necessarily reduce emissions in
the presence of market power
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
Demand response program for smart grid through real time pricing and home energy management system
Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer
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