15,038 research outputs found
Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles
The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified
and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV
management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i
n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe
r important contribution of the
paper is the comparison between deterministic and computational
intelligence techniques to reduce the execution time. The proposed
scheduling is solved with a modified particle swarm optimization.
Mixed integer non-linear programming is also used for comparison purposes. Full ac power
flow calculation is included to allow
taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method
From Packet to Power Switching: Digital Direct Load Scheduling
At present, the power grid has tight control over its dispatchable generation
capacity but a very coarse control on the demand. Energy consumers are shielded
from making price-aware decisions, which degrades the efficiency of the market.
This state of affairs tends to favor fossil fuel generation over renewable
sources. Because of the technological difficulties of storing electric energy,
the quest for mechanisms that would make the demand for electricity
controllable on a day-to-day basis is gaining prominence. The goal of this
paper is to provide one such mechanisms, which we call Digital Direct Load
Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle
individual requests for energy and digitize them so that they can be
automatically scheduled in a cellular architecture. Specifically, rather than
storing energy or interrupting the job of appliances, we choose to hold
requests for energy in queues and optimize the service time of individual
appliances belonging to a broad class which we refer to as "deferrable loads".
The function of each neighborhood scheduler is to optimize the time at which
these appliances start to function. This process is intended to shape the
aggregate load profile of the neighborhood so as to optimize an objective
function which incorporates the spot price of energy, and also allows
distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications
(JSAC): Smart Grid Communications series, to appea
Real-time energy resources scheduling considering intensive wind penetration
The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem
due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with
33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior
Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints
Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method
Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019
A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands
of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector
that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the
potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent
modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the
main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the
time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing.
Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy
prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify
system and market effects effectively
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