Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response

Shams, Mohammad H.; Shahabi, Majid; Khodayar, Mohammad E.

doi:10.1016/j.energy.2018.04.190

Cited by 121 publications

(41 citation statements)

References 26 publications

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“…For high energy conversion efficiency, PV should work at the maximum power point tracking. The maximum output power of a PV array consisting of PV sets can be calculated by (), where I a is radiation (kW/m 2 ), S is total area of arrays (m 2 ), t o is ambient temperature (°C), and η pv is photoelectric conversion efficiency.

P_{pv} = η_{pv} S \cdot I_{a} ((), 1 - 0.005 ((), t_{o} - 25))

…”

Section: System Modellingmentioning

confidence: 99%

“…A comprehensive study on forecasting solar radiation by nonlinear autoregressive model with exogenous inputs (NARX) is conducted in Shams et al, where mean absolute percentage error (MAPE), as described in (), is used for identifying error from forecasted and real values of solar radiations.

italicMAPE = \frac{100}{N} \sum_{i = 1}^{N} (||, \frac{I_{f}^{i} - I_{a}^{i}}{I_{a}^{i}}) %

…”

Section: System Modellingmentioning

confidence: 99%

“…The amount of exchange power between microgrid and upstream grid is defined in (21). If the power is received from upstream network, P Upgrid ωdt is positive and means energy is buying from the upstream network.…”

Section: Power Exchange With Upstream Networkmentioning

confidence: 99%

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Expansion planning of energy storages in microgrid under uncertainties and demand response

BiazarGhadikolaei

Shahabi

Barforoushi

2019

Int Trans Electr Energ Syst

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Summary In this paper, a new stochastic framework is proposed to study expansion planning problem of energy storage systems in microgrids, which contain renewable resources and responsive loads. Fluctuations on wind and solar generation, microgrid islanding capability and load's variations have considered as sources of uncertainties, which are modelled by sets of scenarios. Reserve constrains are considered to cover stochastic nature of uncertainties. Optimization problem is prepared as a two‐stage stochastic programming. Due to presence of discrete and continues variables, mathematical formulations of optimization model are implemented as a mixed integer linear programming (MILP) problem, which are solved by CPLEX solver in GAMS software. A distribution network consist of 33 buses with some responsive loads is selected as microgird to examine the effectiveness of the proposed approach. Results demonstrate that installing storage devices in microgrid cause cost reduction while improving system adequacy. The proposed model can be utilized as a planning tool for microgrids operators, which want to make decisions on choosing different types of storages and allocate them in optimum buses, considering stochastic contingencies and demand response programs.

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P_{pv} = η_{pv} S \cdot I_{a} ((), 1 - 0.005 ((), t_{o} - 25))

…”

Section: System Modellingmentioning

confidence: 99%

italicMAPE = \frac{100}{N} \sum_{i = 1}^{N} (||, \frac{I_{f}^{i} - I_{a}^{i}}{I_{a}^{i}}) %

…”

Section: System Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Expansion planning of energy storages in microgrid under uncertainties and demand response

BiazarGhadikolaei

Shahabi

Barforoushi

2019

Int Trans Electr Energ Syst

Self Cite

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“…In order to surmount the uncertainty of SHPPs and WPPs, the uncertainty should be simulated so as to support the ISO for decision making in day-ahead scheduling [22]. More details about the handling method are in the following article.…”

Section: Definition Of the Uncertainty Setsmentioning

confidence: 99%

Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization

2019

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With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.

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“…In [18], a brief summary of studied papers about operation of photovoltaic based microgrids has been presented to investi-gate various combination methods of energy storage and electric vehicles with other energy resources. It is notewor-thy that optimal scheduling of microgrid systems have been studied under different types of demand response programs in several research papers [19][20][21] which repre-sents essential role of these programs in improvement of optimization results.…”

Section: Introductionmentioning

confidence: 99%

Robust operation of microgrid energy system under uncertainties and demand response program

Barhagh

Shotorbani

Mohammadi‐Ivatloo

et al. 2020

IJEECS

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Microgrid energy systems are one of suitable solutions to the available problems in power systems such as energy losses, and resiliency issues. Local generation by these energy systems can reduce the role of the upstream network, which is a challenge in risky conditions. Also, uncertain behavior of electricity consumers and generating units can make the optimization problems sophisticated. So, uncertainty modeling seems to be necessary. In this paper, in order to model the uncertainty of generation of photovoltaic systems, a scenario-based model is used, while the robust optimization method is used to study the uncertainty of load. Moreover, the stochastic scheduling is performed to model the uncertain nature of renewable generation units. Time-of-use rates of demand response program (DRP) is also utilized to improve the system economic performance in different operating conditions. Studied problem is modeled using a mixed-integer linear programming (MILP). The general algebraic modeling system (GAMS) package is used to solve the proposed problem. A sample microgrid is studied and the results with DRP and without DRP are compared. It is shown that same robustness is achieved with a lower increase in the operation cost using DRP.

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Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response

Cited by 121 publications

References 26 publications

Expansion planning of energy storages in microgrid under uncertainties and demand response

Expansion planning of energy storages in microgrid under uncertainties and demand response

Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization

Robust operation of microgrid energy system under uncertainties and demand response program

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