A Review of Uncertainty Handling Techniques in Smart Grid

Verma, Pranjal; Srinivasan, Dipti; Mehta, Rahul

doi:10.1142/s0218488518500186

Cited by 14 publications

(8 citation statements)

References 93 publications

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“…These uncertainties make the exact prediction or forecast of generation and consumption difficult, thus affecting the reliability and security of the power system. It has been analyzed that there are two types of uncertain parameter, namely, technical parameters and economic parameters . Technical parameters are further classified as operational parameters and topological parameters.…”

Section: Factors To Be Considered For Ems Implementationmentioning

confidence: 99%

“…It has been analyzed that there are two types of uncertain parameter, namely, technical parameters and economic parameters. 139 Technical parameters are further classified as operational parameters and topological parameters. Operational parameters include the information of demand and renewable generation, and topological parameters include the information about the disruption of any line or generating unit.…”

Section: Uncertainty Management Techniquesmentioning

confidence: 99%

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Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Section: Factors To Be Considered For Ems Implementationmentioning

confidence: 99%

Section: Uncertainty Management Techniquesmentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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“…13 This controller can either be an aggregator or an utility, that gathers all information, like energy consumption pattern of the load/consumer, energy production of the DER, and so forth, of each node to run optimization programs toward achieving their goals, for an effective operation. 14 In this area, contributions focus on how solar and wind energy could contribute to the large-scale use, and also on the potential of demand-responsive flexible loads (DR). As an example, for energy systems including large-scale wind energy, a new optimal planning method considering DR is proposed.…”

Section: Energy Management Systemmentioning

confidence: 99%

Energy management in the smart grid: State-of-the-art and future trends

Meliani

Barkany

Abbassi

et al. 2021

International Journal of Engineering Business Management

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Integration of distributed generations that fluctuate widely (such as Photovoltaic panels, Wind power, Electric Vehicles and Energy Storage Systems), poses a chance to the stability of power technology and distribution structures. However, the primary reason is that the electricity ratio between supply and demand may not be balanced. An extra or scarcity inside the production or intake of electricity can disrupt the system and cause critical problems which include a drop/rise in voltage and, under difficult conditions, power outages. The use of Energy Management Systems can effectively increase the balance between supply and demand and decrease peak load throughout unplanned durations. The energy management system is capable of not only sharing or exchanging energy between the different energy resources available, but also of economically supplying loads in a reliable, safe and effective manner under all conditions necessary for the operation of the power grid. This work outlines the structure, goals, benefits and defies of the energy control system via an in-intensity analysis of the distinctive stakeholders and participants engaged on this system. A detailed essential analysis of the functioning of distinct programs which includes Demand Response, Demand Management and Energy Quality Management implemented inside the electricity management gadget is presented in this review. It also summarizes quantifications of the various strategies of uncertainty. It includes as well a comparative and an important assessment of the primary optimization techniques which are used to obtain the extraordinary goals of energy management structures while at the same time meeting a wide range of requirements.

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“…Several approaches have been proposed to handling the uncertainties in the electric power system, and they can be generally classified into robust optimization approaches, probabilistic approaches and interval -set analysis. An updated review of numerous recent approaches can be found in [37,38]. Robust optimization approaches model uncertainties as intervals and involve optimization problems to solve state boundaries.…”

Section: Modeling Of Uncertaintiesmentioning

confidence: 99%

Allocation of FACTS Devices Using a Probabilistic Multi-Objective Approach Incorporating Various Sources of Uncertainty and Dynamic Line Rating

et al. 2020

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Flexible AC transmission system (FACTS) controller play an essential role in increasing the penetration level of renewable energy resources owing to their ability in continuously controlling the active and reactive power flow in the network. This paper presents a probabilistic multi-objective optimization approach to obtain the optimal sizes and locations of static var compensators (SVCs) and thyristor-controlled series capacitors (TCSCs) in a power transmission network with high penetration level of wind generation. The objective of the problem is to maximize the system loadability while minimizing the network power losses and the installation cost of the FACTS controllers. In this study, the uncertainties associated with wind power generation and the correlated load demand are considered. The uncertainties are handled in this work using the points estimation method. Moreover, the dynamic line ratings (DLRs) of the transmission lines are considered in this work. In this case, the maximum transmission capacity of transmission lines is estimated dynamically according to the weather conditions. Considering the DLRs or transmission lines is expected to avoid unrealistic congestion in the network, and hence, improve its loadability. The optimization problem is solved using the multi-objective teaching-learning based optimization (MO-TLBO) algorithm to find the best locations and ratings for the FACTS controllers. Additionally, a technique based on the fuzzy decisionmaking approach is employed to extract one of the Pareto optimal solutions as the best compromise. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results demonstrate the effectiveness of the proposed approach and show that the maximum loadability limit of the study system increases when considering the DLR. This limit can be enhanced to 123.0% without FACTS controller and 137.0 % ,130 % and 132.0% by SVC, TCSC and (SVC-TCSC) respectively.

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A Review of Uncertainty Handling Techniques in Smart Grid

Cited by 14 publications

References 93 publications

Energy management system for smart grid: An overview and key issues

Energy management system for smart grid: An overview and key issues

Energy management in the smart grid: State-of-the-art and future trends

Allocation of FACTS Devices Using a Probabilistic Multi-Objective Approach Incorporating Various Sources of Uncertainty and Dynamic Line Rating

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