Electric energy storage design decision method for demand responsive buildings

Oh, Eunsung; Son, Sung-Yong

doi:10.1016/j.enbuild.2016.05.048

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…A model predictive control-based energy management framework with EES was proposed to reduce electricity expenditure for residual buildings in [19]. In [20], the optimal EES size planning was investigated to minimize the total electricity cost of buildings, including EES investment. EES is also used to support energy-efficient approaches by reducing the uncertainty of renewable sources and demand.…”

Section: B Prior Workmentioning

confidence: 99%

“…p 0 and p t are the demand price and the energy price at time t, respectively. q t is the energy charging/discharging to the EES at time t. The charging/discharging operation is restricted to the EES size e i and can be determined to minimize the electricity bill using a conventional algorithm [20] because the EES operations for the proposed sharing and the conventional individual cases are logically identical.…”

Section: ) Economic Model Of a Participantmentioning

confidence: 99%

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Shared Electrical Energy Storage Service Model and Strategy for Apartment-Type Factory Buildings

Son

2019

IEEE Access

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Electrical energy storage (EES) is a promising and convenient solution for energy efficient buildings, but the high cost of EES limits the expansion of its use. This study presents a shared EES (s-EES) service model including the architecture for implementing the service and a strategy for operating the service for apartment-type factory buildings composed of individually owned units. The proposed architecture for the s-EES service consists of physically connected energy and communication infrastructures, and logical operation of the EES virtually assigned to each participating unit. The conceptual scheme for the s-EES service is presented in the architecture. The service strategy is designed to meet the profit maximization problems of the participants and energy service provider (ESP). The optimal s-EES size and service price conditions are calculated using Lagrangian relaxation, and exchanging information between the participant and the ESP. A case study which uses data from Korea shows that more than 80% of the units in a building participate in the service because of the benefits. The benefits for both the ESP and the units grows with an increase in participating units and a decrease in EES cost. Considering 30%, 50% and 70% EES cost reduction, the ESP's profit increases linearly to about 8, 850, and 1500 dollars a month, respectively. The additional total profit of participants increases exponentially to about 300, 640, 2400 dollars a month, respectively, when compared to individual EES usage. In the s-EES service model, an ESP can provide EES service at a low price through economies of scale, and units in the building can use the EES at a lower price than if they were installed individually.INDEX TERMS Demand-side management, electrical energy storage, energy storage system, energy management system, apartment-type factory, flatted factory, multi-dwelling unit, electric bill.

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Section: B Prior Workmentioning

confidence: 99%

Section: ) Economic Model Of a Participantmentioning

confidence: 99%

Shared Electrical Energy Storage Service Model and Strategy for Apartment-Type Factory Buildings

Son

2019

IEEE Access

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“…Consequently, the role of energy storage in boosting the deployment of renewables is vital [22][23][24][25]. However, energy storage can have a strong influence on the costs of solar and wind electricity, in their effort to meet energy demand in high RES penetration scenarios [26][27][28]. As it is related to the intermittent operation of power plants, the incurrence of such additional costs should not be neglected [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Dispatchable RES Technoeconomics: Is There a Niche for Concentrated Solar Power?

2020

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Raising the penetration of renewable energy sources constitutes one of the main pillars of contemporary decarbonization strategies. Within this context, further progress is required towards the optimal exploitation of their potential, especially in terms of dispatchability, where the role of storage is considered vital. Although current literature delves into either storage per se or the integration of storage solutions in single renewable technologies, the comparative advantages of each technology remain underexplored. However, high-penetration solutions of renewable energy sources (RES) are expected to combine different technological options. Therefore, the conditions under which each technology outperforms their counterparts need to be thoroughly investigated, especially in cases where storage components are included. This paper aims to deal with this gap, by means of assessing the combination of three competing technologies, namely concentrated solar power (CSP), photovoltaics (PV) and offshore wind, with the storage component. The techno-economic assessment is based on two metrics; the levelized cost of electricity and the net present value. Considering the competition between the technologies and the impact storage may have, the paper’s scope lies in investigating the circumstances, under which CSP could have an advantage against comparable technologies. Overall, PVs combined with storage prevail, as the most feasible technological option in the examined storage scenarios—with an LCOE lower than 0.11 €/kWh. CSP LCOE ranged between 0.1327–0.1513 €/kWh for high capacity factors and investment costs, thus larger storage components. Offshore wind—with a lower storage component—had an LCOE of 0.1402 €/kWh. Thus, CSP presents the potential to outperform offshore wind in cases where the latter technology is coupled with high storage requirements. CSP can be viewed as one of the options that could support European Union (EU) decarbonization scenarios. As such, an appropriate market design that takes into consideration and values CSP characteristics, namely dispatchability, is needed at the EU level.

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“…investigated optimal battery sizing for peak shaving, but their exhaustive search algorithm requires the computation of net battery profits using an optimization model at multiple discrete battery sizes to find the global optimum. Oh and Son designed a gradient search algorithm for optimal sizing, but this still requires an optimization model to compute net battery profits at each iteration of battery size. Neither method quantitatively characterizes the trade‐off between battery size (cost) and potential profit.…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical programming methods are useful and accurate,b ut can require significante xpertise and computationalt ime.F or example,O udalov et al [24] investigated optimal battery sizing for peak shaving, but their exhaustive searcha lgorithm requiresthe computation of net battery profits using an optimization model at multipled iscrete battery sizes to find the globalo ptimum. Oh and Son [25] designed ag radient search algorithm for optimal sizing, but this still requires an optimization model to compute net battery profits at each iteration of battery size.N either method quantitatively characterizes the trade-offb etween battery size (cost) and potential profit. Though understanding these methods is useful, this literature does not identify the underlying characteristics of building load shapes that drive battery economics,a nd it does not allow for non-expert stakeholders to calculatep otential revenue or properly size storage resources.…”

Section: Introductionmentioning

confidence: 99%

A Simple Metric for Predicting Revenue from Electric Peak‐Shaving and Optimal Battery Sizing

2018

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A major use case for behind‐the‐meter (BTM) electricity storage is peak‐shaving for commercial and industrial customers who must pay peak‐demand charges. Quantifying the value proposition for individual customers currently requires an optimization model, the development of which can be costly in human and computing resources. We disclose here a simple econometric model to predict revenue from retail peak‐shaving. Geared toward electric utilities, third‐party storage providers, and consumers, this model eliminates the need to formulate a model in specialized optimization software. The model is based on a predictive metric that is derived from the building's load profile. During model fitting, we discovered that the revenue estimates generated are independent of the power capacity of the battery if the maximum power‐to‐energy ratio of the storage is held constant. This effect can be used to calculate the profit‐maximizing storage size, which we explore in a case study.

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Electric energy storage design decision method for demand responsive buildings

Cited by 12 publications

References 21 publications

Shared Electrical Energy Storage Service Model and Strategy for Apartment-Type Factory Buildings

Shared Electrical Energy Storage Service Model and Strategy for Apartment-Type Factory Buildings

A Comparison of Dispatchable RES Technoeconomics: Is There a Niche for Concentrated Solar Power?

A Simple Metric for Predicting Revenue from Electric Peak‐Shaving and Optimal Battery Sizing

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