515 research outputs found
Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power
The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources.
The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model.
The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system
Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling
This paper presents a decision support tool
methodology to help virtual power players (VPPs) in the Smart
Grid (SGs) context to solve the day-ahead energy resource
scheduling considering the intensive use of Distributed
Generation (DG) and Vehicle-To-Grid (V2G). The main focus is
the application of a new hybrid method combing a particle
swarm approach and a deterministic technique based on mixedinteger
linear programming (MILP) to solve the day-ahead
scheduling minimizing total operation costs from the aggregator
point of view. A realistic mathematical formulation, considering
the electric network constraints and V2G charging and
discharging efficiencies is presented. Full AC power flow
calculation is included in the hybrid method to allow taking into
account the network constraints. A case study with a 33-bus
distribution network and 1800 V2G resources is used to
illustrate the performance of the proposed method
Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicles
The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results
Energy resource management under the influence of the weekend transition considering an intensive use of electric vehicles
Energy resource scheduling is becoming increasingly
important, as the use of distributed resources is intensified and of
massive electric vehicle is envisaged. The present paper proposes
a methodology for day-ahead energy resource scheduling for
smart grids considering the intensive use of distributed
generation and Vehicle-to-Grid (V2G). This method considers
that the energy resources are managed by a Virtual Power Player
(VPP) which established contracts with their owners. It takes into
account these contracts, the users' requirements subjected to the
VPP, and several discharge price steps. The full AC power flow
calculation included in the model takes 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
Energy resources management in three distinct time horizons considering a large variation in wind power
The intensive use of distributed generation based on
renewable resources increases the complexity of power
systems management, particularly the short-term scheduling.
Demand response, storage units and electric and
plug-in hybrid vehicles also pose new challenges to the
short-term scheduling. However, these distributed energy
resources can contribute significantly to turn the shortterm
scheduling more efficient and effective improving
the power system reliability.
This paper proposes a short-term scheduling methodology
based on two distinct time horizons: hour-ahead
scheduling, and real-time scheduling considering the
point of view of one aggregator agent. In each scheduling
process, it is necessary to update the generation and
consumption operation, and the storage and electric vehicles
status. Besides the new operation condition, more
accurate forecast values of wind generation and consumption
are available, for the resulting of short-term
and very short-term methods. In this paper, the aggregator
has the main goal of maximizing his profits while,
fulfilling the established contracts with the aggregated
and external players
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