Adaptive robust optimization framework for day-ahead microgrid scheduling

Ebrahimi, Mohammad Reza; Amjady, Nima

doi:10.1016/j.ijepes.2018.11.029

Cited by 72 publications

(49 citation statements)

References 19 publications

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“…In various states of MG operation, a specific measure of probability of reserve sufficiency was kept up in the model. Ebrahimi and Amjady (2019) have utilized the adaptive robust optimization strategy. In MG scheduling model, the authors have displayed a tri-level optimization system for the most pessimistic scenario.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

Kumari

Babu

2020

Transactions of the Institute of Measurement and Control

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This paper presented an effective technique for considering optimal scheduling of microgrid (MG) with islanding constraints. The proposed optimal scheduling approach is the combination of both the modified dragonfly algorithm (MDA) and whale optimization algorithm (WOA)-based approach named as (MDAWO). In this work, the searching conduct of the dragonflies is altered by the effective WOA approach. This WOA is used to the optimal scheduling of MG and also significantly reduces the computational burden. In this paper, the MDAWO is the appraisal technique used to set up the definite schedule of the MG coupling subject to the power assortment. The equality and inequality constraints are utilized to characterize the target capacity of a projected approach using the system information. Batteries act as an energy source to adjust the MG with continual running at an unfaltering and stable output control. Finally, the performance of the proposed technique is executed through the MATLAB/Simulink working platform with two different scenarios. With these two different scenarios, the proposed technique performance is compared with existing techniques such as dragonfly algorithm, firefly and gravitational search algorithm. Furthermore, the statistical analysis of the proposed technique based on cost and fitness are analyzed. Numerical simulations demonstrate the effectiveness of the proposed MG optimal scheduling model and explore its economic and reliability merits.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

Kumari

Babu

2020

Transactions of the Institute of Measurement and Control

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“…To linearize AC power flow equations to determine the worst‐case realization of continuous uncertain parameters in the ARO sub‐problem, (26) and (27) must be substituted with the following equation [10]:

{normalV}_{i, t, ω} = {normalV}_{j, t, ω} - \frac{{normalr}_{i j} {normalP}_{ij, t, ω} + {normalx}_{i j} {normalQ}_{ij, t, ω}}{V_{0, t, ω}} \forall i, t, ω, j \in L_{i j}

where (38) exhibits the relation between the voltage magnitudes of the connected nodes. Therefore, the formulation (12)–(25), (29)–(31), and (38) can be rewritten as follows:

f false(Y_{ω} false) \leq 0 \forall ω, {normalY}_{ω} \in {normalΩ}^{o} : λ

g false(Y_{ω} false) = 0 \forall ω, {normalY}_{ω} \in {normalΩ}^{o} : μ

where (39) and (40) represent the inequality and equality constraints of the third level problem of (1) with linearized AC power flow equations, respectively.…”

Section: Solution Approachmentioning

confidence: 99%

“…The ARO sub‐problem can be represented by a max‐min problem corresponding to the second level and third level of (1), in which the linearized power flow equations are utilized. The lower‐level min problem is replaced by its KKT conditions resulting in a single‐level max problem [10]. Thus, the second level of the proposed decomposition approach ( ARO sub‐problem) can be written as follows:

\underset{{normalΘ}_{t}^{u}, Y_{0}, a_{t}^{U}, a_{t}^{L}, λ, μ, {normalz}^{k k t}}{normalmax} {normalΛ}_{normalII} false(Θ_{t}^{u}, Y_{0}, a_{t}^{U}, a_{t}^{L} false)

Subject to:

{normalΛ}_{normalII} \leq \sum_{t = 1}^{24} false(π_{t}^{e x p e c t e d} P_{t, 0}^{G r i d} + Δ π_{t} false(a_{t}^{U} - a_{t}^{L} false) false) +

\begin{matrix} left \sum_{t = 1}^{24} \sum_{g = 1}^{N_{g}} C_{g} P_{g, t, 0} + \sum_{t = 1}^{24} \sum_{w = 1}^{N_{w}} C_{w} P_{w, t, 0} \\ left + \sum_{t = 1}^{24} \sum_{s = 1}^{N_{s}} {normalC}_{s} {normalP}_{s, t, 0} + \sum <...> \end{matrix}

…”

Section: Solution Approachmentioning

confidence: 99%

Contingency‐constrained operation optimization of microgrid with wind and solar generations: A decision‐driven stochastic adaptive‐robust approach

Ebrahimi

Amjady

2021

IET Renewable Power Gen

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This paper presents a decision‐driven stochastic adaptive‐robust microgrid operation optimization model considering the uncertainties of wind and solar generations, electricity price, and demand as well as the availability uncertainties of microgrid's components. Unlike previous works, this paper utilizes stochastic adaptive‐robust optimization approach to model both continuous and binary uncertainties simultaneously. To do so, adaptive‐robust optimization is used to model the continuous uncertainties, while the binary uncertainties are modelled by means of stochastic programming. An operating dispatchable unit usually exhibits a higher forced outage rate than a de‐committed one. Hence, due to the effect of the optimization decisions on the availability uncertainties, this research work proposes an intrinsic scenario production technique to model these binary uncertainties. In addition, a tri‐level decomposition method is introduced to solve the proposed microgrid operation optimization problem. In this decomposition method, the worst‐case realization of continuous uncertain parameters and unit commitment decisions are determined at each iteration considering the produced scenarios in the previous iteration. Case studies on the IEEE 69‐bus test system exhibit the effectiveness of the proposed decision‐driven stochastic adaptive‐robust model and the proposed solution method.

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“…− P BESS max ≤ P BESS (t) ≤ P BESS max (5) where P BESS max is the maximum power that can be produced by the BESS (kW) at time interval "t", +P means the maximum discharge power, −P means the maximum charge power.…”

Section: Bess Power Outputmentioning

confidence: 99%

“…Microgrids (MG) can combine different kinds of distributed energy resources (DERs) such as distributed generators, distributed storage units, as well as different types of load and control devices [2,3]. For the interactive operation of RES and other MG components, an energy management system (EMS) is required [4,5]. The EMS controls the power flow within the MG by providing references for the DERs based on a predefined objective [6].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

2019

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A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.Energies 2019, 12, 2712 2 of 26 these ESS into small-scale MG architectures. The electrical load profiles of small scale MGs, particularly residential communities, can vary considerably with time: periods of house inoccupancy (e.g., during vacation) and the addition of new equipment (e.g., electric vehicle charge) or even new houses can have a strong influence on the loading profile [9]. Additionally, these small-scale systems see sharp changes in load over a short period of time as single loads (shower, cooker) can be significant considering the size of the MG. For these reasons, EMS used for small scale MGs must have a short control sample time to observe and respond to fast changes in the load and generation throughout the day [9,10]. Also, energy forecasting techniques must be adaptive and also have a short sample time if they are to help the EMS achieve good results for this type of grid.Small-scale MGs should preferably operate as a single controllable unit that imports/exports power from/to the main grid following a predictable shape [11]. In this way, the energy community works for the benefit of the whole grid and not just the small scale MG [12]. To achieve this, a real-time controller is required that allows the small-scale MG to accurately follow a reference value for the power drawn from the main electric grid, where this reference is created by a higher level controller which considers both local and system wide factors.Alternatively, large scale energy storage systems (ESS) (>1 MWh) will play a key role in solving problems such as intermittency of supply and loss of inertia which are challenging electricity grid operation [13], and many grid operators are encouraging the use of ESS to address, for example, increasing demand peaks and network congestion [14].Much of the existing research focusing on microgrid energy management (MGEM) is oriented towards determining the best operating scenario for the MG [15][16][17]. In [18], Carlos et al. introduce a new iterative algorithm that mana...

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Adaptive robust optimization framework for day-ahead microgrid scheduling

Cited by 72 publications

References 19 publications

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

Contingency‐constrained operation optimization of microgrid with wind and solar generations: A decision‐driven stochastic adaptive‐robust approach

Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

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