Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method

Wei, Jingdong; Zhang, Yao; Wang, Jianxue; Cao, Xiaoyu; Khan, Muhammad Armoghan

doi:10.1016/j.apenergy.2019.114188

Cited by 75 publications

(33 citation statements)

References 39 publications

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“…The upper limit of the investment cost during the planning horizon is set by Equation (2) [34,35]. It means the total investment cost cannot exceed the desired investment budget C. As we can see, the annual investment cost is a function of the capital cost c inv i and the installed capacity of candidate plants of each year (i.e., P…”

Section: Budget and Investment Constraintsmentioning

confidence: 99%

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

Wang

Zhang

et al. 2020

Sustainability

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The increasing penetration of renewable energy brings great challenges to the planning and operation of power systems. To deal with the fluctuation of renewable energy, the main focus of current research is on incorporating the detailed operation constraints into generation expansion planning (GEP) models. In most studies, the traditional objective function of GEP is to minimize the total cost (including the investment and operation cost). However, in power systems with high penetration of renewable energy, more attention has been paid to increasing the utilization of renewable energy and reducing the renewable energy curtailment. Different from the traditional objective function, this paper proposes a new objective function to maximize the accommodation of renewable energy during the planning horizon, taking into account short-term operation constraints and uncertainties from load and renewable energy sources. A power grid of one province in China is modified as a case study to verify the rationality and effectiveness of the proposed model. Numerical results show that the proposed GEP model could install more renewable power plants and improve the accommodation of renewable energy compared to the traditional GEP model.

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Section: Budget and Investment Constraintsmentioning

confidence: 99%

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

Wang

Zhang

et al. 2020

Sustainability

Self Cite

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“…Constraint correlations can create problems for chance-constrained programming. The chance-constrained information decision gap method is another way of handling uncertainty [38]. Robust optimization [39] techniques consider the worst-case scenario and also require a transformation of the problem.…”

Section: The Bonus Algorithmmentioning

confidence: 99%

Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

2020

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Renewable energy use can mitigate the effects of climate change. Solar energy is amongst the cleanest and most readily available renewable energy sources. However, issues of cost and uncertainty associated with solar energy need to be addressed to make it a major source of energy. These uncertainties are different for different locations. In this work, we considered four different locations in the United States of America (Northeast, Northwest, Southeast, Southwest). The weather and cost uncertainties of these locations are included in the formulation, making the problem an optimization-under-uncertainty problem. We used the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm to solve these problems. The performance and economic models provided by the System Advisory Model (SAM) system from NREL were used for this optimization. Since this is a black-box model, this adds difficulty for optimization and optimization under uncertainty. The objective function and constraints in stochastic optimization (stochastic programming) problems are probabilistic functionals. The generalized treatment of such problems is to use a two-loop computationally intensive procedure, with an inner loop representing probabilistic or stochastic models or scenarios instead of the deterministic model, inside the optimization loop. BONUS circumvents the inner sampling loop, thereby reducing the computational intensity significantly. BONUS can be used for black-box models. The results show that, using the BONUS algorithm, we get 41%–47% of savings on the expected value of the Levelized Cost of Electricity (LCOE) for Parabolic Trough Solar Power Plants. The expected LCOE in New York is 57.42%, in Jacksonville is 38.52%, and in San Diego is 17.57% more than in Las Vegas. This difference is due to the differences in weather and weather uncertainties at these locations.

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“…In [29] authors proposed a flexible distributed multi-energy power generation system considering uncertainty for long term. Another research work in [30] studied the multi-energy microgrids considering long term and short-term uncertainty. The main objective of both papers is to enhance demand side flexibility by efficiently using distributed multi-energy generation system.…”

Section: Introductionmentioning

confidence: 99%

Structural Condition for Controllable Power Flow System Containing Controllable and Fluctuating Power Devices

2020

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This paper discusses a structural property for a power system to continue a safe operation under power fluctuation caused by fluctuating power sources and loads. Concerns over global climate change and gas emissions have motivated development and integration of renewable energy sources such as wind and solar to fulfill power demand. The energy generated from these sources exhibits fluctuations and uncertainty which is uncontrollable. In addition, the power fluctuations caused by power loads also have the same consequences on power system. To mitigate the effects of uncontrollable power fluctuations, a power flow control is presented which allocates power levels for controllable power sources and loads and connections between power devices. One basic function for the power flow control is to balance the generated power with the power demand. However, due to the structural limitations, i.e., the power level limitations of controllable sources and loads and the limitation of power flow channels, the power balance may not be achieved. This paper proposes two theorems about the structural conditions for a power system to have a feasible solution which achieves the power balance between power sources and power loads. The discussions in this paper will provide a solid theoretical background for designing a power flow system which proves robustness against fluctuations caused by fluctuating power devices.

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Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method

Cited by 75 publications

References 39 publications

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

Structural Condition for Controllable Power Flow System Containing Controllable and Fluctuating Power Devices

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