Optimal scheduling of energy storage under forecast uncertainties

Wang, Zeyu; Negash, Ahlmahz I.; Kirschen, Daniel S.

doi:10.1049/iet-gtd.2017.0037

Cited by 49 publications

(15 citation statements)

References 22 publications

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“…In [39], Helseth et al has solved a medium-term hydro-power scheduling problem considering energy and reserve capacity with the objective of profit maximization. In [40], Wang et al has proposed a storage dispatch along with net capacity dispatched in a day-ahead electricity market using a two-stage optimization method. In [41], a co-operative operation of multiple micro-grids for energy and reserve capacity scheduling has been discussed.…”

Section: Related Workmentioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

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Section: Related Workmentioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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“…The objective function has been calculated considering the Contracted Power is 10 kW. Among the referenced publications for optimal scheduling, [17] and [22] are the newest ones. The method from [22] aims for the same purpose and considers some real-time control.…”

Section: Energies 2019 12 X For Peer Review 11 Of 17mentioning

confidence: 99%

“…The method from [22] aims for the same purpose and considers some real-time control. The method from [17], on the other hand, intends to control a whole distribution grid and would be harder to adapt. Thus, the proposed method is compared to [22], which can be considered the state of the art.…”

Section: Energies 2019 12 X For Peer Review 11 Of 17mentioning

confidence: 99%

“…The simulation is run for the same day, with the same power values for the PV generation and load consumption, and the method from [22] is provided the same prediction as the proposed method had at the beginning of the day, so that they can be compared under the same conditions. Among the referenced publications for optimal scheduling, [17] and [22] are the newest ones. The method from [22] aims for the same purpose and considers some real-time control.…”

Section: Energies 2019 12 X For Peer Review 11 Of 17mentioning

confidence: 99%

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Optimal Scheduling of Energy Storage Using A New Priority-Based Smart Grid Control Method

et al. 2019

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This paper presents a method to optimally use an energy storage system (such as a battery) on a microgrid with load and photovoltaic generation. The purpose of the method is to employ the photovoltaic generation and energy storage systems to reduce the main grid bill, which includes an energy cost and a power peak cost. The method predicts the loads and generation power of each day, and then searches for an optimal storage behavior plan for the energy storage system according to these predictions. However, this plan is not followed in an open-loop control structure as in previous publications, but provided to a real-time decision algorithm, which also considers real power measures. This algorithm considers a series of device priorities in addition to the storage plan, which makes it robust enough to comply with unpredicted situations. The whole proposed method is implemented on a real-hardware test bench, with its different steps being distributed between a personal computer and a programmable logic controller according to their time scale. When compared to a different state-of-the-art method, the proposed method is concluded to better adjust the energy storage system usage to the photovoltaic generation and general consumption.

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“…Herein, the battery degradation cost should be doubled because the ESSs are charged and discharged during the service restoration period when they are selected to act as storage units. To determine role(s) ESSs play in each hour of service restoration, expressions (15) and (16) are formulated, where su ijt is a binary variable which is equal to 1, if the ESS connected to the ith load point become incorporated as a storage unit in tth hour of repair time of the jth contingency. 37 In addition, M is a big number (eg, 100 000 000).…”

Section: Proposed Objective Function For Service Restorationmentioning

confidence: 99%

Reliability improvement in smart grid through incorporating energy storage systems in service restoration

Abniki

Taghvaei²,

Mohammadi‐Hosseininejad

2018

Int Trans Electr Energ Syst

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Summary Smart grid has been developing nowadays as an initiative to operate modern electric distribution systems (EDSs) in a more reliable and efficient manner. With the decreasing price of the energy storage systems (ESSs), ESSs are highly recommended to be exploited in the operation of EDSs. In this manuscript, a comprehensive framework is introduced for ESS incorporation in service restoration process which proposes 2 roles for ESSs in the service restoration. As the first role, ESSs are exploited in the service restoration to re‐energize customers in interrupted zones, as their backup units. As the second role, ESSs are used in the service restoration to increase the number of interrupted customers which transferred to the backup feeders through acting as storage units. The proposed objective function of service restoration consists of customer interruption cost, energy not sold cost, cost of electricity production in combined heat and power units, and cost of ESS incorporation in service restoration. Moreover, the stochastic characteristics of ESS contribution in service restoration are considered in the proposed problem formulation. Further, the impacts of the uncertainties of the service restoration on the proposed approach for ESS incorporation are investigated. The effectiveness of the proposed methodology is deliberated using a standard reliability test system (RBTS‐4). The obtained results show that the incorporating ESSs as storage units will lead to substantially improve the reliability level of EDS through increasing the adequacy of the EDS during service restoration process.

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Optimal scheduling of energy storage under forecast uncertainties

Cited by 49 publications

References 22 publications

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Optimal Scheduling of Energy Storage Using A New Priority-Based Smart Grid Control Method

Reliability improvement in smart grid through incorporating energy storage systems in service restoration

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