Using deferrable demand in a smart grid to reduce the cost of electricity for customers

Jeon, Wooyoung; Lamadrid, Alberto J.; Mo, Jung Youn; Mount, Timothy D.

doi:10.1007/s11149-015-9268-0

Cited by 18 publications

(12 citation statements)

References 15 publications

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“…Complementing previous references, [9] analyzes the overall economical contribution for a wholesale market of being able to control portions (around 10% in that study) of the total demand. An Independent System Operator is assumed, which aims on minimizing total cost (maximizing social welfare).…”

Section: Introductionmentioning

confidence: 81%

“…We assume they can be remotely controlled, so as long as basic power requirements are fulfilled, the gaps of energy to complete those demands constitute control variables just as those of the installed power plant, and they are also used to get the most of the optimization. This is a subtle but fundamental difference with approaches as [9] or [10], which are econometric models based upon historical costs. The model here presented pursues cost savings, but it is founded upon technical constraints (commitment times, operational limits, temporal dependence among variables, etc.…”

Section: Short-term Optimization Modelsmentioning

confidence: 99%

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Benefits of demands control in a smart-grid to compensate the volatility of non-conventional energies

Risso

2019

Rev.Fac.Ing.Univ.Antioquia

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Uruguay is a leader in the usage of renewable energies, getting 96% of its electricity from an assorted assemble of such sources with an increasing percentage of nonconventional energies, of wind power mostly. As clean and financially stable as they are, non-conventional energies have weaknesses. Unlike thermic and most hydro-sources, wind and solar energies are not controllable, are intermittent and uncertain some hours ahead, complicating the short-term operation and maintenance of electrical systems. This work explores how to use smart-grids capabilities to adjust electricity demand as a hedge against novel short-position risks in the Uruguayan electricity market coming from the volatility of non-conventional renewables. This approach uses combinatorial optimization dispatch models to quantitatively assess benefits resulting from having demand control. Results show that for the Uruguayan context, the benefits are not only due to savings in production costs (generation). Smart-grid optimal dispatch-schedules are also less stressing regarding the operation of the grid itself.

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Section: Introductionmentioning

confidence: 81%

Section: Short-term Optimization Modelsmentioning

confidence: 99%

Benefits of demands control in a smart-grid to compensate the volatility of non-conventional energies

Risso

2019

Rev.Fac.Ing.Univ.Antioquia

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“…These inputs are essentially the same as the inputs described in [10], and this section is a summary of Section III of that paper.…”

Section: Specifying Inputs For the Modelmentioning

confidence: 99%

The Case for a Simple Two-Sided Electricity Market

Lamadrid

Jeon

Hao

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Abstract-This paper builds on the results from our earlier research on the design of electricity markets that have to accommodate the uncertainty associated with high penetrations of renewable sources of energy. The key results show that 1) distributed storage (deferrable demand) is an effective way to reduce total system costs, 2) a simple market structure for energy allows aggregators to meet their customers' energy needs and provide ramping services to the system operator, and 3) using a receding-horizon optimization to dispatch units for the next market time-step benefits from the availability of more accurate forecasts of renewable generation and allows market participants to adjust their bids and offers in response to this new information. In our two-sided market, distributed storage in the form of deferrable demand is controlled locally by independent aggregators to minimize their expected payments for energy in the wholesale market, subject to meeting the energy needs of their customers. In addition, these aggregators are responsible for maintaining a stable power factor by installing local capabilities that automatically deal with local power imbalances. Failure to do this triggers penalties paid to the system operator.Our earlier results have shown that it is optimal for an aggregator to submit demand bids into a day-ahead market that include threshold prices for charging and discharging storage and also ensure that the expected amount of stored energy is consistent with the capacity limits of their storage. Because departures from the expected daily pattern of renewable generation are generally persistent (highly positive serial correlated), it is likely that the system operator determines an optimum pattern of demand for the aggregator that violates the capacity limits of storage by the end of the 24-hour period. If the market uses a receding horizon, the results in this paper show that aggregators can modify their bids to ensure that the capacity limits of storage are never violated in the next market time-step.In an empirical application, a stochastic form of multi-period security constrained unit commitment with optimal power flow (the MATPOWER Optimal Scheduling Tool, MOST) using a receding-horizon optimization determines the optimum dispatch and reserves for the next hour and forecasts of the nodal prices for the next 24 hours. The results show that locally controlled deferrable demand is almost as effective as centrally controlled deferrable demand as a way to reduce system costs and mitigate the variability of renewable generation. The additional advantage from using a receding horizon is that the system operator always charges/discharges the storage managed locally by aggregators within the capacity constraints of the storage.

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“…3 The rhythm of development of these distributed energy resources also has an effect in the value of the new "smarter" infrastructure. For instance, demand response (DR) can help system operators to integrate increasing intermittent generation as well as to flatten the load diagram of the networks, allowing to defer the need for costly investments in the expansion of the electrical system (Jeon et al, 2015a(Jeon et al, , 2015bMoslehi and Kumar, 2010). A number of markets in the U.S. are already negotiating DR, and in some cases this is becoming a significant market resource (Rahimi and Ipakchi, 2010).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Regulation on a Firm’s Incentives to Invest in Emergent Smart Grid Technologies

Costa¹,

Bento²,

Marques³

2017

The Energy Journal

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This paper analyzes the implementation of new technologies in network industries through the development of a suitable regulatory scheme. The analysis focuses on Smart Grid (SG) technologies which, among others benefits, could save operational costs and reduce the need for further conventional investments in the grid. In spite of the benefits that may result from their implementation, the adoption of SGs by network operators can be hampered by the uncertainties surrounding actual performances. A decision model has been developed to assess the firms' incentives to invest in "smart" technologies under different regulatory schemes. The model also enables testing the impact of uncertainties on the reduction of operational costs, and of conventional investments. Under certain circumstances, it may be justified to support the development and early deployment of emerging innovations that have a high potential to ameliorate the efficiency of the electricity system, but whose adoption face many uncertainties.

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Using deferrable demand in a smart grid to reduce the cost of electricity for customers

Cited by 18 publications

References 15 publications

Benefits of demands control in a smart-grid to compensate the volatility of non-conventional energies

Benefits of demands control in a smart-grid to compensate the volatility of non-conventional energies

The Case for a Simple Two-Sided Electricity Market

The Impact of Regulation on a Firm’s Incentives to Invest in Emergent Smart Grid Technologies

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