931 research outputs found

    Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation

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    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

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    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

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    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

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    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

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    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

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    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

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    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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