A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty

Zhang, Ning; Chen, Kang; Xia, Qing; Ding, Yi; Huang, Yujing; Sun, Rongguo; Huang, Junhui; Bai, Jing

doi:10.1109/tpwrs.2014.2357816

Cited by 128 publications

(55 citation statements)

References 36 publications

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“…In [10], the pre-contingency cost of security constraints is minimized. In [11], the risk of expected-energy-not-served and WPC are modeled based on the PDF of RD and included in both the operational cost and the constraints. These works only model one of the risk constraints, those are, expected-energynot-served, WPC, and contingency constraints.…”

Section: B Operational Risk Objective Functionmentioning

confidence: 99%

“…However, there is a chance of losing information during scenario reduction. In [11], a risk-based UC formulation is presented to incorporate the risks of the expected-energy-not-served, WPC and branch overflow affected by wind power uncertainty, while neglecting the re-dispatch of conventional TUs for wind power forecasting errors in power flow calculation process. In [12], a chance-constrained programming for handling costbased UC and modeling the day-ahead probability distribution function (PDF) of RD comprising of the WPC influence is presented.…”

Section: Introductionmentioning

confidence: 99%

“…[1], [8], [9], and [11] mainly focus on how to tackle with the uncertainties considering a predefined risk level, which means the risk cannot be adjusted. Different from literatures that explore the SUC operation solutions for minimizing the operational cost and increasing the committed capacity to attain the target risk probability, the suggested model minimizes the operation cost and risk, simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

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Abstract-Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a trade-off between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n-1 security criterion is converted into a risk index. A new bi-objective probabilistic risk/cost based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power re-dispatch process to satisfy up-and-down ramp rate constraints. A new operational cycles based unit commitment algorithm is developed. The approach profits from a new non-dominated sorting backtracking search optimization algorithm for extracting the Pareto optimal set. The proposed approach is shown to provide superior results when applied to two test systems: a) 10-unit and b) IEEE 118-bus, 54-unit system.

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Section: B Operational Risk Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…Risk-based optimization technique- [102,103] Interval optimization method- [101] Robust optimization method- [ …”

Section: Ssm Reactive Power Management-[113]mentioning

confidence: 99%

“…Non-deterministic UC can be divided into four different categories depending on the way in which they address the uncertainty: stochastic programming [90][91][92][93], robust optimization [99,100], interval optimization [101], and risk-based optimization [102,103].…”

Section: Ssm In Isolated Systemsmentioning

confidence: 99%

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Divshali

Choi

2016

Energies

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Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG.

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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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A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty

Cited by 128 publications

References 36 publications

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

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

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