Storage dispatch optimization for grid-connected combined photovoltaic-battery storage systems

Nottrott, A.; Kleissl, Jan; Washom, B.

doi:10.1109/pesgm.2012.6344979

Cited by 42 publications

(21 citation statements)

References 5 publications

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“…The output of wind farms ( ) that can be securely integrated is calculated based on the wind forecast potential output ( ) and time-ahead curtailment ( ) in Equation (8). Instead of using the wind forecast output to calculate the secondary and tertiary reserves requirement, the wind output that can be integrated is applied in Equation (9) to determine the system reserve requirement, Therefore, a new dynamic reserve modelling method is proposed to release part of the system flexibility at high renewable energy penetration levels. First of all, the proposed reserve method is only applied to the wind reserve component in Equation (7), because the wind farms are more geographically concentrated, compared with the PV plants, and most of them are connected to the transmission network, which can give the system operator direct control over the wind curtailment beforehand based on system operational conditions.…”

Section: Dynamic Reservementioning

confidence: 99%

“…The output of wind farms (w t ) that can be securely integrated is calculated based on the wind forecast potential output (W t ) and time-ahead curtailment (ω t ) in Equation (8). Instead of using the wind forecast output to calculate the secondary and tertiary reserves requirement, the wind output that can be integrated is applied in Equation (9) to determine the system reserve requirement, Energies 2017, 10, 989 9 of 34 which would allow the system operator to find a balance between reserve provision and renewable integration to minimise the system operational cost:…”

Section: Dynamic Reservementioning

confidence: 99%

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System-Level Operational and Adequacy Impact Assessment of Photovoltaic and Distributed Energy Storage, with Consideration of Inertial Constraints, Dynamic Reserve and Interconnection Flexibility

et al. 2017

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Abstract:The growing penetration of solar photovoltaic (PV) systems requires a fundamental understanding of its impact at a system-level. Furthermore, distributed energy storage (DES) technologies, such as batteries, are attracting great interest owing to their ability to provide support to systems with large-scale renewable generation, such as PV. In this light, the system-level impacts of PV and DES are assessed from both operational and adequacy perspectives. Different control strategies for DES are proposed, namely: (1) centralised, to support system operation in the presence of increasing requirements on system ramping and frequency control; and (2) decentralised, to maximise the harnessing of solar energy from individual households while storing electricity generated by PV panels to provide system capacity on request. The operational impacts are assessed by deploying a multi-service unit commitment model with consideration of inertial constraints, dynamic reserve allocation, and interconnection flexibility, while the impacts on adequacy of supply are analysed by assessing the capacity credit of PV and DES through different metrics. The models developed are then applied to different future scenarios for the Great Britain power system, whereby an electricity demand increase due to electrification is also considered. The numerical results highlight the importance of interconnectors to provide flexibility. On the other hand, provision of reserves, as opposed to energy arbitrage, from DES that are integrated into system operation is seen as the most effective contribution to improve system performance, which in turn also decreases the role of interconnectors. DES can also contribute to providing system capacity, but to an extent that is limited by their individual and aggregated energy availability under different control strategies.

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Section: Dynamic Reservementioning

confidence: 99%

Section: Dynamic Reservementioning

confidence: 99%

System-Level Operational and Adequacy Impact Assessment of Photovoltaic and Distributed Energy Storage, with Consideration of Inertial Constraints, Dynamic Reserve and Interconnection Flexibility

et al. 2017

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“…The reason for this interest is that these emerging technologies may contribute to reducing peak demand and network congestion, minimizing disturbances and increasing network reliability [1][2][3][4][5]. As the technology matures, economic benefits may result from reducing capital and maintenance expenditures and deferring network augmentation [2,[5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Similar to the conventional electricity generation and supply system, these technologies will rely on accurate forecasting of future electricity demand [5,[10][11][12]. The electricity demand forecasts will need to provide information on how much power is required to be generated at certain times, the scheduling of charging and discharging of energy storage systems, and be able to determine whether or not there are adequate resources to meet future demand with decision points to activate remedial measures such as load shedding, etc.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Bennett

Stewart

2014

Energies

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This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R 2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.

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“…While this work was being conducted, a research has been available in the literature by Fallahi, Nick, Riahy, Hosseinian, and Doroudi (2014) that addressed optimal storage allocation problem. There is also research being conducted in nonlinear optimization techniques for storage applications Bose et al (2012) Hu et al (2012) Nottrott, Kleissl, andWashom (2012) Xu andSingh (2012) Geth, Tant, Haesen, Driesen, andBelmans (2010) while our scope here is focused on linear optimization. The Gotland has been chosen as test study which will be reviewed in the next section.…”

Section: Introductionmentioning

confidence: 99%

Storage commitment and placement for an interconnected island system with high wind penetration, Gotland

Erduman

Uzunoğlu

2014

2014 International Conference on Renewable Energy Research and Application (ICRERA)

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The objective of this study is to investigate storage commitment and storage placement problem for island system that has intermittency of wind power generation in a diurnal wind pattern. This objective will be achieved through minimization of cost while conducting Optimal Power Flow (OPF) analysis Fu, Shahidehpour, and Li (2006). In addition to the methodology introduced by Fu et al. (2006) and Wang, Shahidehpour, and Li (2008), impact of storage will be implemented in this model to generation and demand to analysis the dispatching electric storage systems based on different intermittent generation scenarios for an island system while addressing optimal placement.The case studied in this paper which is Gotland Island has a project in place to upgrade the existing power system to a smart grid. Regional weak power grid and a combination of wind power generation, household loads and some major industries puts on challenges on the island grid structure. HVDC connection to the Swedish mainland balances the grid while in an emergency situation, emergency generators in the weak parts of grid are dispatched Ackermann (2005).Storage systems will be addressed through their contributions to demand or generation implemented. Optimal placement of storage will be implemented through the objectives of minimizing cost of generation Bose, Gayme, Topcu, and Chandy (2012) Ghofrani, Arabali, Etezadi-Amoli, andFadali (2013) while addressing transmission line constraints. The solutions will be analyzed based on scenarios for the hourly volatility of wind power in simulated diurnal scenarios Fu et al. (2006) and Wang et al. (2008). Storage problem will be formulated as part of the cost optimization problem with the relevant power system and equipment constraints.

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Storage dispatch optimization for grid-connected combined photovoltaic-battery storage systems

Cited by 42 publications

References 5 publications

System-Level Operational and Adequacy Impact Assessment of Photovoltaic and Distributed Energy Storage, with Consideration of Inertial Constraints, Dynamic Reserve and Interconnection Flexibility

System-Level Operational and Adequacy Impact Assessment of Photovoltaic and Distributed Energy Storage, with Consideration of Inertial Constraints, Dynamic Reserve and Interconnection Flexibility

Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Storage commitment and placement for an interconnected island system with high wind penetration, Gotland

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