Look-Ahead Bidding Strategy for Energy Storage

Wang, Yishen; Dvorkin, Yury; Fernández-Blanco, Ricardo; Xu, Bolun; Qiu, Ting; Kirschen, Daniel S.

doi:10.1109/tste.2017.2656800

Cited by 122 publications

(76 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equations (8) and (9) enforce the power balance for the AC and DC buses where load shedding is not allowed. The power flow through the interfacing converter is formulated in constraints (10)- (12). Due to the bidirectional conversion loss, binary variables z DC s,t are used to indicate the flow direction with big-M constraints as in [12], [25].…”

Section: A Commercial Building Hybrid Microgrid Configurationmentioning

confidence: 99%

“…Interfacing between the utility grid and customers, microgrids are effective solutions to provide multiple services to both parties with reduced operating costs [3], [4], [5], improved reliability [6], [7], [8] and enhanced resiliency [9], [10], [11]. Optimal DER sizing to determine energy storage [12], [13], PV [14], [15] and demand response [16], [17], [18] capacities are essential to gain all the benefits microgrids can offer. Chen et al [3] sized energy storage from a cost-benefit analysis with microgrid unit commitment scheduling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Distributed Energy Resources Sizing for Commercial Building Hybrid Microgrids

Wang

Shi

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

As microgrids have advanced from early prototypes to relatively mature technologies, converting data center integrated commercial buildings to microgrids provides economic, reliability and resiliency enhancements for the building owners. Thus, microgrid design and economically sizing distributed energy resources (DER) are becoming more demanding to gain widespread microgrids commercial viability. In this paper, an optimal DER sizing formulation for a hybrid AC/DC microgrid configuration has been proposed to leverage all benefits that AC or DC microgrid could solely contribute. Energy storage (ES), photovoltaics (PV) and power electronics devices are coordinately sized for economic grid-connected and reliable islanded operations. Time-of-use (TOU) energy usages charges and peak demand charges are explicitly modeled to achieve maximum level of cost savings. Numerical results obtained from a real commercial building load demonstrate the benefits of the proposed approach and the importance of jointly sizing DER for the grid-connected and islanded modes. NOMENCLATURE A. Sets and IndicesS Set of representative days, indexed by s. T Set of time intervals, indexed by t. B. Decision variables c inv Total investments. c e/d s Energy/demand charges at day s. c lcl/lnl s Lost of critical/non-critical load costs at day s. c deg s Energy storage degradation costs at day s. x P V Installed PV capacities. x ES Installed batteries power ratings. x IC Installed interfacing converter capacities. x IN V Installed inverter capacities. x CON Installed converter capacities. p grid s,t Energy purchased from utility at day s time t. p peak s Net load peak demand at day s. lcl AC/DC s,t Critical AC/DC load shedding at day s time t. lnl AC/DC s,t Non-critical AC/DC load shedding at day s time t. v DC s,t PV DC output at day s time t. dch AC/DC s,t ES AC/DC discharge at day s time t. ch AC/DC s,t ES AC/DC charge at day s time t. soc s,t ES state-of-charge at day s time t. This work is funded by SGCC Science and Technology Program. f AC s,t AC bus flow at day s time t. f DCin s,t DC bus injection at day s time t. f DCout s,t DC bus extraction at day s time t. z DC s,t Binary indicator for DC bus flow direction at day s time t, 1 for injection, 0 otherwise. y ES s,t Binary indicator for ES discharging status at day s time t, 1 for discharging, 0 otherwise. vi DC s,t Islanded PV DC output at day s time t. dchi AC/DC s,t Islanded ES AC/DC discharge at day s time t. f i AC s,t Islanded AC bus flow at day s time t. f i DCin s,t Islanded DC bus injection at day s time t. f i DCout s,t Islanded DC bus extraction at day s time t. zi DC s,t Islanded DC bus flow direction indicator at day s time t, 1 for injection, 0 otherwise. u ES s,t /κ ES s,t Auxiliary variables for ES discharging state. C. Parameters π s Probability of representative day s. C P V Annualized investment for PV. C ES Annualized investment for ES. C IC Annualized investment for interfacing converters. C IN V Annualized investment for AC/DC inverters. C CON Annualized investment for D...

show abstract

Section: A Commercial Building Hybrid Microgrid Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Distributed Energy Resources Sizing for Commercial Building Hybrid Microgrids

Wang

Shi

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results are compared to other different storage options. A bi-level optimization approach is presented in [12], which optimizes an energy storage operator's bidding strategy for the day-ahead and the following day market. Determining the optimal installation place and the size of storage devices have been widely studied.…”

Section: Using Energy Storage Technologiesmentioning

confidence: 99%

The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey

et al. 2017

View full text Add to dashboard Cite

Renewable energies as a solution for environmental issues have always been a key research area due to Demand Response Programs (DRPs). However, the intermittent nature of such energies may cause economic and technological challenges for Independent System Operators (ISOs) besides DRPs, since the acceptable effective solution may exceed the requirement of further investigations. Although, previous studies emphasized employing Demand Response and Renewable Energies in power systems, each problem was investigated independently, and there have been few studies which have investigated these problems simultaneously. In these recent studies, authors neither analyzed these problems simultaneously nor discussed which scientific and practical aspects of demand response and renewable energy injection were employed. Motivated by this requirement, this research has focused on a comprehensive review of recent research of these cases to provide a comprehensive reference for future works.

show abstract

“…From the perspective of transmission system operators, load bus represents the aggregated load for all the downstream loads within the sub-transmission and distribution systems [1]. Currently, there is an increasing trend to deploy distributed resources in the system, such as PV [2], demand response [3], EV [4], and energy storage [5]. In addition, AC microgrids [6], DC microgrids [7] or hybrid AC/DC microgrids [8], [9] can also be viewed as aggregated loads.…”

Section: Introductionmentioning

confidence: 99%

Submodular Load Clustering with Robust Principal Component Analysis

Wang

Xu³

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.

show abstract

Look-Ahead Bidding Strategy for Energy Storage

Cited by 122 publications

References 37 publications

Optimal Distributed Energy Resources Sizing for Commercial Building Hybrid Microgrids

Optimal Distributed Energy Resources Sizing for Commercial Building Hybrid Microgrids

The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey

Submodular Load Clustering with Robust Principal Component Analysis

Contact Info

Product

Resources

About