Assessing the Impact of Price Forecast Errors on the Economics of Distributed Storage Systems

Ahlert, Klaus-Henning; Block, Carsten

doi:10.1109/hicss.2010.72

Cited by 11 publications

(11 citation statements)

References 23 publications

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“…regression algorithm relies within the extent of the regression function fitting the dataset [10]. Checks are thus imposed into this model by using the Curve fitting algorithm, to make sure that the fitting curve does not exceed to the limit of hampering the whole system [11].…”

Section: B Linear Regression Techniquementioning

confidence: 99%

Load Prediction in Smart Grid Networks

Sivakoti¹

2015

IJCDS

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Efficient forecasting and load prediction for maintaining the accurate DR (Demand Response) ratio is a key factor in implementing and deploying the Smart-Grid networks [1]. There are a plethora of techniques and models suggested by forecasters over the decades, the most accurate and feasible beingartificial neural networks, linear regression technique and the curve fitting algorithm. Researchers have demonstrated extreme zeal and effort in devloping algorithms which could derive the best effeciency, thus saving excess production than demand. For example, the work descrbied in the paper [2] puts forward the prediction values to be at an accuracy of around 95%. A hybrid algorithm has been presented in this paper, which has been practically proved to have a forecasting efficiency much higher than the conventional methods. Using the artificial neural networks for training the model with historical data and fluctuations in demand, the linear regression method has been used for implementing the temperature sensitivity, namelydew point, humidity, wind speed, seasonal variations and location of the smart-meter. Together along with the curve fitting algorithm, the proposed hybrid algorithm has been practically implemented by taking data from smart-meters across the United States to determine their efficiency of implementation. The proposed algorithm described in this paper encountered a marvelous prediction accuracy of 99.2%-99.45%, which promises vast reduction in the power wasted by power utility companies owing to the mismatch within the DR rates from the consumer end and is far accurate than the predictions made by [2].

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Section: B Linear Regression Techniquementioning

confidence: 99%

Load Prediction in Smart Grid Networks

Sivakoti¹

2015

IJCDS

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“…The formulation can be modified to include spin and non-spin reserve capacities in practical applications. The operational relations and constraints are shown in (1) to (6).…”

Section: Single Day Bes Scheduling Modelmentioning

confidence: 99%

“…The kW output, losses and stored energy are related by (1); losses and current loading by (2); charging and discharging limits by (3); stored energy limit by (4). Equations (5) and (6) dictate that the available regulation capacity (up and down) are determined by the stored energy at the proceeding hour:…”

Section: Single Day Bes Scheduling Modelmentioning

confidence: 99%

“…However, boundary conditions are not discussed explicitly for dayahead timeframes and simulation results show that dispatch solutions always have depleted the charges at the last hour of dispatch days [3 and 5]. Ahlert [6] studied impact of price forecast errors on the optimal scheduling and concluded that the forecasting errors do not have a significant impact to the overall benefits of BES devices. For scheduling operations, a simple circuit model having internal resistance and voltage source on a BES device level was presented in [7].…”

Section: Introductionmentioning

confidence: 99%

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A Method of Modeling Boundary Conditions for Multi-Period Battery Energy Storage Dispatch

Hao¹

2012

Power and Energy Systems and Applications

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This paper evaluates the economic impacts of initial and final charges of battery energy storage (BES) devices on dispatch solutions when used in a multi-day and recurring operation. Two new modeling methods are proposed that have the ability of capturing the impacts of initial and final charges: one has a 2-day rolling scheduling window; and the other is a proposed model with a single day scheduling window, but incorporates the economic impact of ending charges on the next scheduling day. Numerical simulations are presented to illustrate their characteristics in term of dispatch solutions and economic impacts.

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“…In addition, the distributed storage system has the potential to reduce the electricity cost of the household (Ahlert and Block, 2010). The hydrogen storage concept has been studied from an investment perspective and it was found that the use of hydrogen storage for electricity generation is uneconomical (Taljan et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The impact of electricity storage on wholesale electricity prices

Nyamdash

Denny

2013

Energy Policy

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This paper analyzes the impact of electricity storage on the production cost of a power system and the marginal cost of electricity (electricity price) using a unit commitment model. Also real world data has been analyzed to verify the effect of storage operation on the electricity price using econometric techniques. The unit commitment model found that the deployment of a storage system reduces the fuel cost of the power system but increases the average electricity price through its effect on the power system operation. However, the reduction in the production cost was found to be less than the increase in the consumer's cost of electricity resulting in a net increase in costs due to storage. Different storage and CO 2 price scenarios were investigated to study the sensitivity of these results. The regression analysis supports the unit commitment results and finds that the presence of storage increases average wholesale electricity prices for the case study system.

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Assessing the Impact of Price Forecast Errors on the Economics of Distributed Storage Systems

Cited by 11 publications

References 23 publications

Load Prediction in Smart Grid Networks

Load Prediction in Smart Grid Networks

A Method of Modeling Boundary Conditions for Multi-Period Battery Energy Storage Dispatch

The impact of electricity storage on wholesale electricity prices

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