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If prices appear to be in a channel and you only want to buy if the market trades above a resistance point, then you might place a buy stop above that area to enter a long position if the market moves that high In other words, you are waiting until the market proves that it may go higher by surpassing a certain price or chart point Of course, the opposite is true for a sell stop If you only want to sell once the market price trades below a support point, you would place a sell stop below that area to enter a short position, waiting until the market proves that it may go lower by surpassing a certain price or chart point..

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size is given by Spanel , and it is rated in watts at peak solar insolation. B(k) is de ned as the residual battery energy stored at time k , and Bmax is de ned to be the total battery capacity. The load energy demand is given by L(k) over the time duration [(k 1) , k ], and therefore the energy balance equation can be written as [2] B(k) = min{max[B(k 1) + Epanel (k) L(k), Boutage ], Bmax } (12.1)

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where Boutage is the maximum allowed depth of battery discharge, based on safety and battery life considerations [3]. When B(k) < Boutage , the charge controller will disconnect the MAP/MP load and the node will experience a radio outage. Note that Eq. (12.1) can easily be modi ed to include non-ideal effects such as battery temperature dependence. Since the total received solar energy and the traf c related workload are stochastic processes, this results in a level of uncertainty that must be addressed when the node is provisioned. The performance of an SMAP can be measured in terms of the frequency of system outage, POut . Using public solar insolation data for a particular geographic location [4, 5] and the energy ow model, a worst-case design can be performed. This is done by assuming that L(k) = PAPmax for all k, where PAPmax is the peak power consumption of the SMAP. Using Eq. (12.1), a discrete time simulation of the node is done over the solar insolation history of the desired location. Using this method, it is straightforward to produce a set of solar panel versus battery capacity contours for a constant outage probability, POut . In order to complete this process, a solar energy conversion model is required that translates available solar insolation records into solar panel inputs for a given geographic location. A brief overview of the conversion model used is given in the Appendix. An example output from such a simulation is shown in Figure 12.4 for two example locations (i.e., Toronto, Ontario, Canada, and Phoenix, Arizona), assuming a constant

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single-radio SMAP power consumption of 1 W The graphs show plots of solar panel size versus battery capacity for curves of constant outage probability Comparing the two geographic locations, the differences in node con guration are seen to be very large In the more temperate case (Toronto), provisioning a solar node for negligible outage (ie, < 10 4 ) requires about a 22-W solar panel, which alone would be a signi cant fraction of the total node cost In the Phoenix case the same performance can be obtained with about an 8-W panel This will obviously result in a signi cant node cost difference between the two locations For each outage probability in Figure 124, there is a continuum of feasible battery/panel combinations In this chapter we assume that the panel/battery selection is done on the basis of minimum cost.

Stop close only stop orders are elected only if prices in the final closing range (usually the final minute of trading) are below your sell stop or above your

(12.2)

A simple cost model is developed that relates the power consumption, the target outage probability, and the overall battery and solar panel costs Using the approximate linear relationship between cost and both solar panel and battery size (which is usually the case), lines of constant cost and slope can be superimposed on Figure 124 The optimum panel and battery cost corresponds to the line that intersects the desired outage curve tangentially We now consider the minimum cost of various design options For comparison we will express these costs in Canadian dollars (CAD) using recent single unit retail costs We assume that the variable component of the cost function will be made up of the costs of the battery and the solar panel This component relies on the target outage probability, POut , and the power consumption pro le associated with the node.

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