Purpose-The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google. Design/methodology/approach-The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in-links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non-optimized web pages for comparison. Findings-The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites. Research limitations/implications-Only the top 50 web sites for a specific query were analyzed. Analysing more web sites and comparing with similar studies in different competition would provide more concrete results. Practical implications-The paper offers a revealing insight into the techniques used by industry experts to rank highly in Google, and the success or otherwise of those techniques. Originality/value-This paper fulfils an identified need for web sites and e-commerce sites keen to attract a wider web audience.
We consider the problem of dispatching a fleet of distributed energy reserve devices to collectively meet a sequence of power requests over time. Under the restriction that reserves cannot be replenished, we aim to maximise the survival time of an energy-constrained islanded electrical system; and we discuss realistic scenarios in which this might be the ultimate goal of the grid operator. We present a policy that achieves this optimality, and generalise this into a set-theoretic result that implies there is no better policy available, regardless of the realised energy requirement scenario.
We consider the problem of dispatching a fleet of heterogeneous energy storage units to provide grid support. Under the restriction that recharging is not possible during the time frame of interest, we develop an aggregate measure of fleet flexibility with an intuitive graphical interpretation. This analytical expression summarises the full set of demand traces that the fleet can satisfy, and can be used for immediate and straightforward determination of the feasibility of any service request. This representation therefore facilitates a wide range of capability assessments, such as flexibility comparisons between fleets or the determination of a fleet's ability to deliver ancillary services. Examples are shown of applications to fleet flexibility comparisons, signal feasibility assessment and the optimisation of ancillary service provision. NOMENCLATURE nTotal number of devices D i ith device N Set of all devices e i (t)Energy of the ith device at time t u i (t)Power output of the ith device at time t u(t) Vector of power outputs at time t, across all devices p i Maximum power rating of the ith devicē pVector of maximum power ratings, across all devices P Diagonal matrix of maximum power ratings, across all devices UpProduct set of power constraints, with vector of maximum powersp x i (t), z i (t) Time-to-go of the ith device at time t x(t), z(t) Vector of time-to-go values at time t, across all devices X State-space X(t) ). This work was supported by EPSRC studentship 1688672. P r (t)Reference at time t P r [t0,t)
The increasing reliance on renewable energy generation means that storage may well play a much greater role in the balancing of future electricity systems. We show how heterogeneous stores, differing in capacity and rate constraints, may be optimally, or nearly optimally, scheduled to assist in such balancing, with the aim of minimizing the total imbalance (unserved energy) over any given period of time. It further turns out that in many cases the optimal policies are such that the optimal decision at each point in time is independent of the future evolution of the supply–demand balance in the system, so that these policies remain optimal in a stochastic environment. This article is part of the theme issue ‘The mathematics of energy systems’.
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