DSO congestion management using demand side flexibility

Esmat, Ayman; Usaola, Julio

doi:10.1049/cp.2016.0797

Cited by 10 publications

(16 citation statements)

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“…Research that articulates local flexibility and prosumers has emerged in recent years. One common practice is to directly include local flexibility scheduling in distribution power flow calculations to solve voltage and congestion issues [8]- [12]. This approach assumes that the DSO and aggregator form a unique entity, and decisions about flexibility exploitation are driven by grid state analysis through power flows.…”

Section: B Current Researchmentioning

confidence: 99%

“…In addition, our approach differs from [16], in that: 1) we include uncertainty effects and storage systems; 2) we solve the problem from the perspective of residential storage aggregation and not from MV-level DG standpoint; and 3) in the sense that we include a local flexibility trading strategy, in addition to the local constraint support. Finally, in contrast to [8]- [12] we do not consider power flow calculations to be part of the flexibility aggregator's tasks.…”

Section: B Current Researchmentioning

confidence: 99%

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Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets

Flórez

Michiorri

Kariniotakis

2020

IEEE Trans. Smart Grid

103

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This paper presents an optimization model for Home Energy Management Systems from an aggregator's standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means of electric water heaters. Resources are managed in order to participate in the day-ahead energy and local flexibility markets, also considering grid constraint support at the Point of Common Coupling. The resulting model is a Mixed-Integer Linear Programming problem in which the objective is to minimize day-ahead operation costs for the aggregator while complying with energy commitments in the day-ahead market and local flexibility requests. Three sources of uncertainty are considered: energy prices, PV production and load. Adjustable Robust Optimization is used to find a robust counterpart of the problem for including uncertainty. The results obtained show that using robust optimization allows strategic bidding to capture uncertainties while complying with obligations in the wholesale and local market. Data from a real-life energy community with 25 households is used to validate the proposed robust bidding methodology.

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Section: B Current Researchmentioning

confidence: 99%

Section: B Current Researchmentioning

confidence: 99%

Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets

Flórez

Michiorri

Kariniotakis

2020

IEEE Trans. Smart Grid

103

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“…Centralized energy management could be provided by an electricity retailer or a demand-side aggregator who could (i) participate in the electricity exchange market with power demand bids (Campaigne and Oren 2016), and (ii) offer load shifting-based grid support services to the local Distribution System Operator (DSO) (Esmat and Usaola 2016).…”

Section: Energy Managementmentioning

confidence: 99%

Automated energy management in distributed electricity systems: An EEPOS approach

Purviņš

́Abbate

2017

International Journal of Green Energy

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The increasing capacity of distributed electricity generation brings new challenges in maintaining a high security and quality of electricity supply. New techniques are required for grid support and power balance. The highest potential for these techniques is to be found on the part of the electricity distribution grid. This article addresses this potential and presents the EEPOS project's approach to the automated management of flexible electrical loads in neighborhoods. The management goals are (i) maximum utilization of distributed generation in the local grid, (ii) peak load shaving/congestion management, and (iii) reduction of electricity distribution losses. Contribution to the power balance is considered by applying two-tariff pricing for electricity. The presented approach to energy management is tested in a hypothetical sensitivity analysis of a distribution feeder with 10 households and 10 photovoltaic (PV) plants with an average daily consumption of electricity of 4.54 kWh per household and a peak PV panel output of 0.38 kW per plant. Energy management shows efficient performance at relatively low capacities of flexible load. At a flexible load capacity of 2.5% (of the average daily electricity consumption), PV generation surplus is compensated by 34-100% depending on solar irradiance. Peak load is reduced by 30% on average. The article also presents the load shifting effect on electricity distribution losses and electricity costs for the grid user.

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“…Grid congestion management solutions appear in various forms and address different aspects of grid congestion problems [4], [5], [6], [7], [8]. However, while grid constraints raise questions concerning priority when conflicts of use arise, these studies on congestion management do not take into account an explicit notion of fairness.…”

Section: Introductionmentioning

confidence: 99%

Fairness in Smart Grid Congestion Management

Hekkelman

Poutré

2019

2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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With the energy transition, grid congestion is increasingly becoming a problem. This paper proposes the implementation of fairness in congestion management by presenting quantitative fair optimization goals and fairness measuring tools. Furthermore, this paper presents a congestion management solution in the form of an egalitarian allocation mechanism. Finally, this paper proves that this mechanism is truthful, pareto efficient, and maximizes a fair optimization goal.

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DSO congestion management using demand side flexibility

Cited by 10 publications

References 0 publications

Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets

Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets

Automated energy management in distributed electricity systems: An EEPOS approach

Fairness in Smart Grid Congestion Management

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