Facilitation of renewable electricity using price based appliance control in Ireland’s electricity market

Finn, P.; Fitzpatrick, Colin; Connolly, David; Leahy, Martin J.; Relihan, L.

doi:10.1016/j.energy.2011.02.038

Cited by 72 publications

(38 citation statements)

References 5 publications

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“…The Irish government has set ambitious targets in 2007, for its energy usage: no oil in electricity generation by 2020, 15% of electricity from renewable resources by 2010, and 33-40% by 2020. Wind is the most abundant renewable energy source available in Ireland [16,17]. However, introducing such renewable energy sources increases volatility in the market, making energy price prediction and cost-efficient planning considerably more challenging.…”

Section: Electricity Price Forecasting For the Irish Marketmentioning

confidence: 99%

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Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

Grimes

Ifrim

O’Sullivan

et al. 2014

Sustainable Computing: Informatics and Systems

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Publication information AbstractEnergy cost-aware scheduling, i.e., scheduling that adapts to real-time energy price volatility, can save large energy consumers millions of dollars every year in electricity costs. Energy price forecasting coupled with energy priceaware scheduling, is a step towards this goal. In this work, we study cost-aware schedules and the effect of various price forecasting schemes on the end schedule-cost. We show that simply optimizing price forecasts based on classical regression error metrics (e.g., Mean Squared Error), does not work well for scheduling. Price forecasts that do result in significantly better schedules, optimize a combination of metrics, each having a different impact on the end-schedulecost. For example, both price estimation and price ranking are important for scheduling, but they carry different weight. We consider day-ahead energy price forecasting using the Irish Single Electricity Market as a case-study, and test our price forecasts for two real-world scheduling applications: animal feed manufacturing and home energy management systems. We show that price forecasts that co-optimize price estimation and price ranking, result in significant energy-cost savings. We believe our results are relevant for many real-life scheduling applications that are currently plagued with very large energy bills.

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Section: Electricity Price Forecasting For the Irish Marketmentioning

confidence: 99%

“…The battery level, l i,t , is calculated using Equation (16), where z c i is the charge efficiency of the battery. Finally the net energy usage of the electric vehicle, E ev t , in time period t is given by Equation (17), where z d i is the discharge efficiency of the EV battery i. (17) 4.2.3.…”

Section: Electric Vehiclementioning

confidence: 99%

Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

Grimes

Ifrim

O’Sullivan

et al. 2014

Sustainable Computing: Informatics and Systems

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“…It also makes unit commitment and economic dispatch problems more complicated, which are assessed in [7]. Studies show that in some countries in 2020, up to 13% of trade periods will require wind curtailment [8], indicating high wind generation uncertainty. According to [9], a forecasting horizon…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Storage Using Residential Hot Water Heaters

Gelažanskas

Gamage

2016

Energies

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This paper proposes and analyses a new demand response technique for renewable energy regulation using smart hot water heaters that forecast water consumption at an individual dwelling level. Distributed thermal energy storage has many advantages, including high overall efficiency, use of existing infrastructure and a distributed nature. In addition, the use of a smart thermostatic controller enables the prediction of required water amounts and keeps temperatures at a level that minimises user discomfort while reacting to variations in the electricity network. Three cases are compared in this paper, normal operation, operation with demand response and operation following the proposed demand response mechanism that uses consumption forecasts. The results show that this technique can produce both up and down regulation, as well as increase water heater efficiency. When controlling water heaters without consumption forecast, the users experience discomfort in the form of hot water shortage, but after the full technique is applied, the shortage level drops to nearly the starting point. The amount of regulation power from a single dwelling is also discussed in this paper.

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“…In, e.g., Finn et al (2011) facilitation of wind generated electricity by price optimized thermal storage was described. In contrast to the current centralized power generation system, the electricity grid will be a network of many independent power generators.…”

Section: Introductionmentioning

confidence: 99%

Nonconvex model predictive control for commercial refrigeration

Hovgaard

Boyd

Larsen

et al. 2013

International Journal of Control

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We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real-time.We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

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Facilitation of renewable electricity using price based appliance control in Ireland’s electricity market

Cited by 72 publications

References 5 publications

Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

Distributed Energy Storage Using Residential Hot Water Heaters

Nonconvex model predictive control for commercial refrigeration

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