Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory

Hooshmand, Ehsan; Rabiee, Abbas

doi:10.1049/iet-gtd.2018.5671

Cited by 58 publications

(37 citation statements)

References 36 publications

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“…Step 3: Obtain the probability distribution of general load and electric vehicle load using (10) and (12) respectively and the power generated from solar power plant using (11).…”

Section: Methodsmentioning

confidence: 99%

“…In the literature, ESU planning is formulated as an optimization problem and is solved by using the analytical approach, conventional optimization, and meta-heuristic approach. In [10], [11], RESs and ESUs are optimally placed in a distribution network using meta-heuristic algorithms to face the uncertainty of RESs and also to tackle demand response. In [12], a multi-objective optimization model based on particle swarm optimization (PSO) is proposed considering peak shaving, voltage quality, and dynamic power adjustment.…”

Section: Hesumentioning

confidence: 99%

“…Here, the hourly time-variation of general load and solar PV are introduced by scaling factors constructed using the historical data. The real power of the general load and power generated from solar PV plant connected at the i th location (bus) at hour h of the day in f th n year is given by (10) and (11) respectively. As mentioned earlier, the reactive power is calculated using the power factor.…”

Section: Problem Formulation a Ress And Load Modelingmentioning

confidence: 99%

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Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

2020

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In recent days, aiming for power generation with less carbon emission, led to the high penetration of renewable energy into the distribution grid. To improve the intermittency caused by renewables and to increase the grid flexibility, grid integrated Energy Storage Units (ESUs) are proposed as the promising solution in the literature. However, considering the investment cost, ESUs are optimally placed by satisfying the network electrical constraints. On the other hand, consideration of environmental impacts and other practical constraints are also equally important. Therefore, in this article, on top of grid performance parameters, realistic parameters that may affect the location and its size such as (1) environmental impact, (2) land requirement & its associated cost for ESU installation and (3) renewable purchase obligation are formulated in the objective function. Decision making on ESU installation is a planning activity, which follows uncertainty. Consequently, it is essential to model the uncertainty parameters into the objective function, for better planning. In this article, optimal planning of hybrid ESUs based on realistic parameters along with uncertainty is addressed. For this study, a practical 156-bus distribution system of Dehradun district, India is considered. From the results obtained, it is evident that, formulating hybrid ESU constrained with the environmental impact has significantly decreased the emission of CO2 with maximum grid stability. INDEX TERMS Batteries, distributed power generation, environmental economics, power system planning, sustainable development.

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“…Step 3: Obtain the probability distribution of general load and electric vehicle load using (10) and (12) respectively and the power generated from solar power plant using (11).…”

Section: Methodsmentioning

confidence: 99%

Section: Hesumentioning

confidence: 99%

Section: Problem Formulation a Ress And Load Modelingmentioning

confidence: 99%

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Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

2020

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“…To consider the uncertainties of both wind units and PV systems, information gap decision theory is employed in [70] in which demand response and storage models have been developed to enhance the system performance within the mentioned uncertainties. In a similar research in [71], wind- Because of large scale renewable generation, wind farms with several wind-turbines should be prepared for each ramp event of wind turbines.…”

Section: • Transmission and Distribution Sectormentioning

confidence: 99%

Application of information gap decision theory in practical energy problems: A comprehensive review

2019

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The uncertainty and risk modeling is a hot topic in operation and planning of energy systems. The system operators and planners are decision makers that need to handle the uncertainty of input data of their models. As an example, energy consumption has always been a critical problem for operators since the forecasted values, and the actual consumption is never expected to be the same. The penetration of renewable energy resources is continuously increasing in recent and upcoming years.These technologies are not dispatch-able and are highly dependent on natural resources. This would make the real-time energy balancing more complicated. Another source of uncertainty is related to energy market prices which are determined by the market participants' behaviors. To consider these issues, the uncertainty modeling should be performed. Various approaches have been previously utilized to model the uncertainty of these parameters such as probabilistic approaches, possibilistic approaches, hybrid possibilisticprobabilistic approach, information gap decision theory, robust and interval optimization techniques. This paper reviews the research works that used information gap decision theory for uncertainty modeling in energy and power systems.

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“…In [7], the optimal power dispatch in a smart distribution network is studied, considering demand response and wind generation, while in [8] a price-aware residential DR model, which incorporates renewable sources and the electric vehicle, is presented. The optimal allocation of renewable sources, energy storage and demand response is also analyzed in [9], aiming to reduce via information gap decision theory the unintended costs due to the uncertainties of RESs.…”

Section: Introductionmentioning

confidence: 99%

Economic Benefits of Energy Storage and Price-aware Demand Response for Future Smart Cities

Picioroaga

Eremia

Sănduleac

2019

2019 54th International Universities Power Engineering Conference (UPEC)

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As new goals were set in terms of reliability and sustainability in urban power grids, demand response (DR) programs have gained remarkable attention for their benefits offered to both grid and customers. The scope of this work is to analyze the impact of different types of demand side management (DSM) programs in Smart City context, considering the presence of local renewable energy sources (RESs) and storage devices. In this paper, a scheduling optimization scheme for an urban distribution network is presented, aiming to reduce the total operation cost. In this regard, a dual-objective model has been developed, that influences the balance between the global operational cost and the total load curtailment provided by the DR programs, while satisfying the technological constraints of the system.

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Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory

Cited by 58 publications

References 36 publications

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

Application of information gap decision theory in practical energy problems: A comprehensive review

Economic Benefits of Energy Storage and Price-aware Demand Response for Future Smart Cities

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