931 research outputs found
Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation
To effectively harness the significant flexibility from massive distributed
energy resources (DERs) for transmission-distribution interaction, power
flexibility aggregation is performed for a distribution system to compute the
feasible region of the exchanged power at the substation. Based on the adaptive
robust optimization (ARO) framework, this paper proposes a novel methodology
for aggregating system-level power flexibility, considering heterogeneous DER
facilities, network operational constraints, and unbalanced power flow model.
In particular, two power flexibility aggregation models with two-stage
optimization are developed for application: one focuses on aggregating active
power and computes its optimal feasible intervals over multiple periods, while
the other solves the optimal elliptical feasible regions for the aggregate
active-reactive power. By leveraging ARO technique, the disaggregation
feasibility of the obtained feasible regions is guaranteed with optimality. The
numerical simulations conducted on a real-world distribution feeder with 126
multi-phase nodes demonstrate the effectiveness of the proposed method.Comment: 8 Page
An Exact Characterisation of Flexibility in Populations of Electric Vehicles
Increasing penetrations of electric vehicles (EVs) presents a large source of
flexibility, which can be used to assist balancing the power grid. The
flexibility of an individual EV can be quantified as a convex polytope and the
flexibility of a population of EVs is the Minkowski sum of these polytopes. In
general computing the exact Minkowski sum is intractable. However, exploiting
symmetry in a restricted but significant case, enables an efficient computation
of the aggregate flexibility. This results in a polytope with exponentially
many vertices and facets with respect to the time horizon. We show how to use a
lifting procedure to provide a representation of this polytope with a reduced
number of facets, which makes optimising over more tractable. Finally, a
disaggregation procedure that takes an aggregate signal and computes dispatch
instructions for each EV in the population is presented. The complexity of the
algorithms presented is independent of the size of the population and
polynomial in the length of the time horizon. We evaluate this work against
existing methods in the literature, and show how this method guarantees
optimality with lower computational burden than existing methods
Heat FlexOffers:a device-independent and scalable representation of electricity-heat flexibility
The increasing relevance of Renewable Energy Sources (RES) makes energy flexibility an extremely important aspect, not only regarding electricity, but also for other energy vectors such as heat. Because of this, there is the need for a flexibility model which can i) provide a common representation of flexibility for different device types, ii) perform aggregation, optimization and disaggregation while scaling for long time horizons and many devices, iii) capture most of the available flexibility, and iv) support energy conversion between different vectors. Properties i)-iii) are addressed by FlexOffer (FO), a device-independent model that describes energy constraints in an approximate yet accurate way. This paper proposes an extension of FOs, Heat FlexOffers (HFOs), capable of modeling flexibility for different energy vectors such as heat and handling energy conversion, and therefore addressing iv) as well as i)-iii). HFOs can model the optimal power curve for heat pumps, and can provide constraints for continuous optimization problems while complying to the Smart Grid-Ready (SG-Ready) interface, which operates on discrete states. We show that HFOs are very accurate, being able to retain up to of total flexibility before aggregation and of it after aggregation. HFOs aggregation is scalable, as 2 · 10^6 devices can be aggregated for a 24 hours time horizon, vastly outperforming exact models as they fail to aggregate more than 500 devices.</p
A Projection-Based Approach for Distributed Energy Resources Aggregation
Aggregating distributed energy resources (DERs) is of great significance to
improve the overall operational efficiency of smart grid. The aggregation model
needs to consider various factors such as network constraints, operational
constraints, and economic characteristics of the DERs. This paper constructs a
multi-slot DER aggregation model that considers the above factors using
feasible region projection approach, which achieved the protection of DERs data
information and the elimination of internal variables. A system economic
dispatch (ED) model is established for the operators to make full use of the
DER clusters. We calculate the feasible regions with temporal coupling by
extending the Progressive Vertex Enumeration (PVE) algorithm to high dimension
by the Quickhull algorithm. Finally, an IEEE 39-bus distribution network is
simulated with DERs to verify the effectiveness of the proposed model. Results
show that the two-step ED derives the same results as the centralized ED
Identifying Secure Operating Ranges for DER Control using Bilevel Optimization
Active distribution grids are accommodating an increasing number of
controllable electric loads and distributed energy resources (DERs). A majority
of these DERs are managed by entities other than the distribution utility, such
as individual customers or third-party aggregators, who control the loads and
DERs without consideration of any distribution grid constraints. This makes it
challenging for a distribution system operator (DSO) to allow third-party
aggregators and transmission operators to fully exploit the flexibility offered
by these resources while also ensuring that distribution grid constraints such
as voltage magnitude limits are not violated. In this paper, we develop a
bilevel optimization-based framework to determine the aggregate power
flexibility that can be obtained from an unbalanced distribution grid while
ensuring that there is no disaggregation solution that leads to grid constraint
violations. The results are a set of constraints and operating rules that are
easy to communicate, and which provide the entities that procure flexibility
from DERs (e.g. transmission operators or third-party aggregators) with the
ability to freely implement their own disaggregation strategy without
intervention from the DSO. The proposed approach is tested on two unbalanced
distribution feeders and our simulation results indicate that it is possible to
determine a wide range of aggregate power flexibility, as long as a simple set
of rules for DER control activation are followed
An Efficient Method for Quantifying the Aggregate Flexibility of Plug-in Electric Vehicle Populations
Plug-in electric vehicles (EVs) are widely recognized as being highly
flexible electric loads that can be pooled and controlled via aggregators to
provide low-cost energy and ancillary services to wholesale electricity
markets. To participate in these markets, an EV aggregator must encode the
aggregate flexibility of the population of EVs under their command as a single
polytope that is compliant with existing market rules. To this end, we
investigate the problem of characterizing the aggregate flexibility set of a
heterogeneous population of EVs whose individual flexibility sets are given as
convex polytopes in half-space representation. As the exact computation of the
aggregate flexibility set -- the Minkowski sum of the individual flexibility
sets -- is known to be intractable, we study the problems of computing
maximum-volume inner approximations and minimum-volume outer approximations to
the aggregate flexibility set by optimizing over affine transformations of a
given convex polytope in half-space representation. We show how to
conservatively approximate the pair of maximum-volume and minimum-volume set
containment problems as linear programs that scale polynomially with the number
and dimension of the individual flexibility sets. The class of approximations
methods provided in this paper generalizes existing methods from the
literature. We illustrate the improvement in approximation accuracy achievable
by our methods with numerical experiments.Comment: 10 pages, 4 figure
Virtus project: A scalable aggregation platform for the intelligent virtual management of distributed energy resources
open5noVIRTUS Project, funded by Cassa per i Servizi Energetici e Ambientali (Fund for Energy and Environmental Services-CSEA)-Project code CCSEB_00094.The VIRTUS project aims to create a Virtual Power Plant (VPP) prototype coordinating the Distributed Energy Resources (DERs) of the power system and providing services to the system operators and the various players of the electricity markets, with a particular focus on the industrial sector agents. The VPP will be able to manage a significant number of DERs and simulate realistic plants, components, and market data to study different operating conditions and the future impact of the policy changes of the Balancing Markets (BM). This paper describes the project’s aim, the general structure of the proposed framework, and its optimization and simulation modules. Then, we assess the scalability of the optimization module, designed to provide the maximum possible flexibility to the system operators, exploiting the simulation module of the VPP.openBianchi S.; De Filippo A.; Magnani S.; Mosaico G.; Silvestro F.Bianchi S.; De Filippo A.; Magnani S.; Mosaico G.; Silvestro F
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