The Potential of Intermittent Renewables to Meet Electric Power Demand: Current Methods and Emerging Analytical Techniques

Hart, Elaine K.; Stoutenburg, Eric D.; Jacobson, Mark Z.

doi:10.1109/jproc.2011.2144951

Cited by 117 publications

(61 citation statements)

References 59 publications

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“…32)]. These are described as 'first order' demand-balance simulations (see Hart et al (2012)) and could be regarded as high-level, exploratory studies rather than the 'second-order' system-level reliability analyses typically carried out by electrical system operators.…”

Section: Renewable Simulation Literature Reviewmentioning

confidence: 99%

A Framework for Incorporating EROI into Electrical Storage

Palmer

2017

Biophys Econ Resour Qual

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was modelled as a limiting case of VRE plus storage, and is therefore not intended as a comprehensive cost-optimised solution to high-penetration VRE. A shift from an electrical system based mostly on energy stocks to one based mostly on natural flows is constrained by the quantity of storage required, and the quantity of VRE overbuild to charge the stores. The application of the framework shows that the value of electrical storage and overbuild exhibits a marked diminishing returns behaviour at rising VRE penetration and therefore the first units of storage are the most valuable. The framework is intended to stimulate further research into using EROI to better understand the role of VRE and storage in prospective energy transitions.

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Section: Renewable Simulation Literature Reviewmentioning

confidence: 99%

A Framework for Incorporating EROI into Electrical Storage

Palmer

2017

Biophys Econ Resour Qual

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“…The bus data and line data had altered with the cause of renewable energy sources. While renewable energy sources linked with a bus and the bus will supposed to be a generator bus [9]. Newton raphson method of power flow in some pattern volume is available [10].…”

Section: IImentioning

confidence: 99%

“…These elements move around the multi-dimensional search area till they find best location [16].Every particle has its personal best location called pbest and one location best from overall swarm is known as gbest. With the result of top values, the revised elements with its velocity and locations are given with these two equations (8) and (9). at t iteration the velocity of ith particle, = a t iteration the location of ith particle,…”

Section: B Particle Swarm Optimization Algorithmmentioning

confidence: 99%

PSO Algorithm based Loss Minimization Approach for Optimal Placement and Sizing of Renewable Energy Source

Kaur¹

2017

IJRASET

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In this research paper, PSO is joint with Newton Raphson method of power flow to optimize optimal location along with size of renewable energy source. A NR method is used to measure the losses and find the voltage at each bus. The PSO is used to locate the best location as well as sizing of renewable energy source. The main aim is to reduce the losses and keep the voltage profile acceptable. IEEE 30 bus standard system is used for the observations. There is the comparison of results of system without renewable energy source and with renewable energy source by some methods of optimization. Wind farm is considered as the renewable energy source.

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“…Now let us explain how the production risk emerges for a fossil fuel power generator. Variations in energy demand are typically limited and more predictable compared with the variations in supply (Nyamdash et al, 2010;Hart et al, 2012;Ummels et al, 2007). However, due to the low operating cost of intermittent RE that leads to its earlier dispatch (Denholm et al, 2010), the residual load is intermittent.…”

Section: Frugalitymentioning

confidence: 99%

Precautionary Storage in Electricity Markets

Durmaz

2016

SSRN Journal

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As renewable energy depends on meteorological shocks and is non-controllable, the overall energy production becomes riskier with the rising renewable share. Although this has led to a renewed interest in storage technologies, not much consideration has been given to energy storage due to precautionary motives. In our study, we look at to what extent a convex marginal utility (prudence) and a convex marginal cost (frugality) can spur precautionary energy storage. We set up a simple theoretical model of energy consumption and production with intermittent renewable sources, dispatchable systems, and energy storage. First, we characterize the optimum and demonstrate how prudence and frugality can lead to higher levels of energy storage. By applying our findings to perfectly competitive markets, we further show that prudence and frugality increase the market energy price through higher demand for energy storage and decrease price volatility. Our analysis can have implications for inventory decisions in various other industries where firms face capacity constraints and are exposed to production risks.

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The Potential of Intermittent Renewables to Meet Electric Power Demand: Current Methods and Emerging Analytical Techniques

Cited by 117 publications

References 59 publications

A Framework for Incorporating EROI into Electrical Storage

A Framework for Incorporating EROI into Electrical Storage

PSO Algorithm based Loss Minimization Approach for Optimal Placement and Sizing of Renewable Energy Source

Precautionary Storage in Electricity Markets

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