Optimal Market Participation of Distributed Load Resources Under Distribution Network Operational Limits and Renewable Generation Uncertainties

Sadeghi-Mobarakeh, Ashkan; Shahsavari, Alireza; Haghighat, H.; Mohsenian‐Rad, Hamed

doi:10.1109/tsg.2018.2830751

Cited by 23 publications

(8 citation statements)

References 36 publications

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Section: A Context and Literature Reviewmentioning

confidence: 99%

“…In [5], it has been demonstrated that the optimal dispatch plan of distributed resources results in an infeasible or costly outcome when the ADN's hard security constraints are ignored. As a result, robust optimization in [5] has been written with ADN's hard security constraints. In [6,7], the robust optimization method has been used to mitigate the ADN's congestion by reducing the variance of daily branch power flow while accounting for end-users' consumption and PV system production uncertainties.…”

Section: A Context and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Distributionally Robust Chance Constrained Optimization for Providing Flexibility in an Active Distribution Network

Rayati¹,

Bozorg²,

Cherkaoui³

et al. 2022

IEEE Trans. Smart Grid

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In this paper, we propose a distributionally robust chance constrained (DRCC) optimization problem for the operation of an active distribution network (ADN). The ADN's operator uses the proposed problem to centrally optimize the dispatch plan of his resources, namely photovoltaic (PV) and battery energy storage (BES) systems, and to participate in wholesale real/reactive power and flexibility markets. We model the uncertainties in the problem by knowing a set of probability distributions, i.e., an ambiguity set. The uncertainties include production capability of PV systems, end-users' consumption, requested flexibility by the external network's operator, and voltage magnitude at the point of common coupling (PCC). The resulting formulation is a DRCC optimization problem for which a solution methodology based on freely available solvers is presented. We evaluate the performance of proposed solution in the numerical results section by comparing it with two benchmark models based on stochastic and chance constrained (CC) optimization.

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Section: A Context and Literature Reviewmentioning

confidence: 99%

Section: A Context and Literature Reviewmentioning

confidence: 99%

Distributionally Robust Chance Constrained Optimization for Providing Flexibility in an Active Distribution Network

Rayati¹,

Bozorg²,

Cherkaoui³

et al. 2022

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…where, Score area, pro denotes endpoint area of protection subjects including DHH (DALYs, disability-adjusted life years), DEH (species year, potentially disappeared fraction of species m 2 year or potentially disappeared fraction of species m 3 year), and DRA (USD, extra costs for future mineral and fossil resource extraction) [46], f midpoint, endpoint denotes endpoint characteristics factor transversal vector derived from various midpoint impact categories, M denotes the normalization midpoint impact column vectors derived from normalization, which can be expressed as follows: Water consumption WCP - 16 Mineral resource MRP kg Cu-eq/kg ore 17…”

Section: Calculating Life Cycle Evaluation Value Through Normalizatiomentioning

confidence: 99%

“…Distributed renewable energy system (DRES), which integrates various renewable energy power generation technologies, can optimize the allocation and utilization of energy, and have better environmental performance when compared to fossil energy production options, is a pretty foundation and solid support for energy internet [10].Renewable energy is highly praised for its wide availability and environmental friendliness, as well as its decisive advantage over traditional energy is that it is not subjected to fossil fuel resources depletion and it does not lead to much increasing pollution [11]. The feasibility and potential for DRES have been performed by many researchers in terms of technical [12][13][14], economic [15][16][17], and ecological characteristics [18][19][20]. Kasperowicz et al [21] presented the possibility of estimation of an appropriate power supply based on renewable energy sources in the context of the whole energy system in the annual balance taking into account the technical and economic optimization strategies.…”

mentioning

confidence: 99%

Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets

Wang

Zhang³

et al. 2019

Energies

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The distributed renewable energy system, integrating various renewable energy resources, is a significant energy supply technology within energy internet. It is an effective way to meet increasingly growing demand for energy conservation and environmental damage reduction in energy generation and energy utilization. In this paper, the life cycle assessment (LCA) method and fuzzy rough sets (FRS) theory are combined to build an environmental evaluation model for a distributed renewable energy system. The ReCiPe2016 method is selected to calculate the environmental effect scores of the distributed energy system, and the FRS is utilized to identify the crucial activities and exchanges during its life cycle from cradle to grave. The generalized evaluation method is applied to a real-world case study, a typical distributed energy system located in Yanqing District, Beijing, China, which is composed of wind power, small-scale hydropower, photovoltaic, centralized solar thermal power plant and a biogas power plant. The results show that the environmental effect of per kWh power derived from the distributed renewable energy system is 2.06 × 10−3 species disappeared per year, 9.88 × 10−3 disability-adjusted life years, and 1.75 × 10−3 USD loss on fossil resources extraction, and further in the uncertainty analysis, it is found that the environmental load can be reduced effectively and efficiently by improving life span and annual utilization hour of power generation technologies and technology upgrade for wind turbine and photovoltaic plants. The results show that the proposed evaluation method could fast evaluate the environmental effects of a distributed energy system while the uncertainty analysis with FRS successfully and effectively identifies the key element and link among its life span.

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“…For these reasons, in the last decade RO has been applied to several optimization fields, including power systems. Regarding DERs, RO has been often used to model the uncertain behaviour of load consumption and RES production within specific problems: the optimal energy storage system location [16], the optimal demand bidding under uncertain market and including distribution network operational limits [17], the generation and transmission expansion planning problem [18]. When dealing with the flexibility provided by DERs, RO has been applied from the aggregator point of view for maximizing the flexibility potential of its customers to provide services to other actors [19] and to minimize day-ahead operation costs while complying with energy commitments in the day-ahead market and local flexibility requests [20].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

et al. 2021

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Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure.

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Optimal Market Participation of Distributed Load Resources Under Distribution Network Operational Limits and Renewable Generation Uncertainties

Cited by 23 publications

References 36 publications

Distributionally Robust Chance Constrained Optimization for Providing Flexibility in an Active Distribution Network

Distributionally Robust Chance Constrained Optimization for Providing Flexibility in an Active Distribution Network

Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets

Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

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