Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios.
Abstract:
Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.
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TL;DR: The experiment results show that the HEMS based on the price prediction reduces the daily electricity expenditures by 22.2% as well as the peak-to-average-radio (PAR) by 35.5% in the load demand.
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Q1. What contributions have the authors mentioned in the paper "Optimal residential load control with price prediction in real-time electricity pricing environments" ?
This is particularly true if the utility companies provide price information only one or two hours ahead of time.
Q2. What is the common scenario for a load reduction request?
Load reduction requests are usually sent out by the utilities when electricity demand is high enough to put the grid reliability at risk, or rising demand requires the imminent activation of expensive/unreliable generation sets.
Q3. What is the main reason why PHEVs are becoming popular?
As PHEVs become popular, there is an increasing interest in using the storage capacity of their batteries to return someenergy back to the grid when needed [37].
Q4. What are the benefits of real-time pricing?
Although real-time pricing has several potential advantages, its benefits are currently limited due to lack of efficient building automation systems as well as users’ difficulty in manually responding to time-varying prices.
Q5. How can the authors improve price prediction accuracy?
Price prediction accuracy can further improve by using longer and more computationally complicated price prediction filters, if needed.
Q6. How long does it take to charge a PHEV?
As another example, the user may select αa = 10 PM and βa = 7 AM (the next day) for his PHEV after plugging it in at night such that the battery charging finishes by early morning time when he needs to use the vehicle to go to work.
Q7. How can the user balance payment and wait time for each appliance?
Recall from Section II-C that the user can balance payment and waiting time for the operation of each household appliance by adjusting parameter δa for each appliance a.
Q8. Why is the price function ph(lh) not tractable?
Note that optimization problem (15) is not tractable in its current form due to the non-differentiability of the price function ph(lh) in (6).
Q9. What is the reformulated version of optimization problem for the case with multiple utility companies?
the reformulated version of optimization problem (25) for the case with multiple utility companies would still be a linear program.