A Generic Storage Model Based on a Future Cost Piecewise-Linear Approximation

Alvarez, Manuel; Rönnberg, Sarah; Bermudez, J.; Bollen, Math

doi:10.1109/tsg.2017.2754288

Cited by 15 publications

(13 citation statements)

References 19 publications

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“…Losses materialise because of the self-discharge of the ESS over time. When modelling the ESS short-term operation, such as participation in the day-ahead or intraday markets, this term is often ignored [16,17], but it is taken into account in the long-term models [18].…”

Section: Energy Storage System Modelsmentioning

confidence: 99%

“…Pandžić et al [66] analysed the influence of the optimisation period on profits of an ESS operator and concluded that it is best for a virtual storage plant to optimise operation over the scheduling horizon of at least two days. Alvarez et al [18] used the future cost function to model the influence of ESS' long-term strategy on its short-term operation. They showed that fixing the ESS' state of energy to the previously scheduled long-term amounts increases operational costs and therefore decreases profit of the virtual power plant (VPP).…”

Section: Self-schedulingmentioning

confidence: 99%

“…Ciftci et al [47] modelled ESS used for load following and energy arbitrage within a microgrid with the objective of energy cost minimisation. Alvarez et al [18] compared behaviour of a virtual power plant with and without and ESS and showed that ESS does increase the VPP's profit.Liu et al [97] optimised a cloud ESS in a microgrid and define service pricing mechanisms for it. The microgrid in this paper contains households with rooftop PVs and a cloud energy storage that trades with them.…”

Section: Distribution System-level Essmentioning

confidence: 99%

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Operating and Investment Models for Energy Storage Systems

2020

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In the context of climate changes and the rapid growth of energy consumption, intermittent renewable energy sources (RES) are being predominantly installed in power systems. It has been largely elucidated that challenges that RES present to the system can be mitigated with energy storage systems (ESS). However, besides providing flexibility to intermittent RES, ESS have other sources of revenue, such as price arbitrage in the markets, balancing services, and reducing the cost of electricity procurement to end consumers. In order to operate the ESS in the most profitable way, it is often necessary to make optimal siting and sizing decisions, and to determine optimal ways for the ESS to participate in a variety of energy and ancillary service markets. As a result, many publications on ESS models with various goals and operating environments are available. This paper aims at presenting the results of these papers in a structured way. A standard ESS model is first outlined, and that is followed by a literature review on operational and investment ESS models at the transmission and distribution levels. Both the price taking and price making models are elaborated on and presented in detail. Based on the examined body of work, the paper is concluded with recommendations for future research paths in the analysis of ESS.

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Section: Energy Storage System Modelsmentioning

confidence: 99%

Section: Self-schedulingmentioning

confidence: 99%

Section: Distribution System-level Essmentioning

confidence: 99%

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Operating and Investment Models for Energy Storage Systems

2020

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“…Alvarez et al [29] proposed a general purpose ESS model inspired by the behavior of hydraulic reservoirs. Using the results obtained from stochastic dual DP, carried out to determine the long-term energy schedule, the linear model of ESS cost is derived.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where each element g (i,t,k) is an integer between −1 and +1, depending on the time (t) and NL value (P N(t) ). Similarly, the population or group of agents of the optimization algorithm for iteration k can be expressed as a matrix according to (29):…”

Section: Problem Formulationmentioning

confidence: 99%

Day-Ahead Optimal Battery Operation in Islanded Hybrid Energy Systems and Its Impact on Greenhouse Gas Emissions

et al. 2019

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This paper proposes a management strategy for the daily operation of an isolated hybrid energy system (HES) using heuristic techniques. Incorporation of heuristic techniques to the optimal scheduling in day-head basis allows us to consider the complex characteristics of a specific battery energy storage system (BESS) and the associated electronic converter efficiency. The proposed approach can determine the discharging time to perform the load peak-shaving in an appropriate manner. A recently proposed version of binary particle swarm optimization (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, is proposed for day-ahead scheduling determination. Day-ahead operation and greenhouse gas (GHG) emissions are studied through different operating conditions. The complexity of the optimization problem depends on the available wind resource and its relationship with load profile. In this regard, TVMS-BPSO has important capabilities for global exploration and local exploitation, which makes it a powerful technique able to provide a high-quality solution comparable to that obtained from a genetic algorithm.Incorporation of ESS can improve the accommodation of renewable generation while reducing greenhouse gas (GHG) emissions. As an example, incorporation of renewable power combined with ESS in California could reduce carbon dioxide (CO 2 ) emissions from 90% to 72%, whereas renewable power curtailment reduces from 33% to 9%. In the case of Texas, CO 2 emissions could be reduced from 58% to 54% and renewable power curtailment could be reduced from 3% to 0.3% [4]. Combination of carbon capture and storage devices with conventional generation units is also an option to reduce GHG emissions. However, the combination of renewable generation with ESS can be energetically more effective [5].Historically, the acquisition costs of a battery energy storage system (BESS) have been considerably high, limiting their economic performance and consequently their mass adoption. However, when very low GHG emissions are required, BESS can be a critical device to achieve such an ambitious goal.In the case of energy provision for an isolated hybrid energy system (HES), incorporation of BESS becomes profitable due to the fact that the fuel consumption and operating hours of a conventional generator are considerably reduced. In the case of a grid-connected HES, retailing rates and feed-in tariffs as well as favorable resources are crucial for their successful adoption [6].Heuristic techniques are commonly used to carry out the optimal sizing of a specific HES. Consequently, some of them have been implemented in computational programs such as HOMER Pro ® [7], iHOGA ® [8], and Hybrid2 ® [9], among others. Dispatch strategies implemented in most of these tools are based on load following and cycle charging concepts. Load following consists of generating power from conventional units only to satisfy net load (NL), and this approach is frequently suggested in a HES with high share of renewable power, which is much h...

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