31,967 research outputs found
A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids
Cooperating interconnected microgrids with the Distribution System Operation
(DSO) can lead to an improvement in terms of operation and reliability. This
paper investigates the optimal operation and scheduling of interconnected
microgrids highly penetrated by renewable energy resources (DERs). Moreover, an
efficient stochastic framework based on the Unscented Transform (UT) method is
proposed to model uncertainties associated with the hourly market price, hourly
load demand and DERs output power. Prior to the energy management, a newly
developed linearization technique is employed to linearize nodal equations
extracted from the AC power flow. The proposed stochastic problem is formulated
as a single-objective optimization problem minimizing the interconnected AC MGs
cost function. In order to validate the proposed technique, a modified IEEE 69
bus network is studied as the test case
On Robust Tie-line Scheduling in Multi-Area Power Systems
The tie-line scheduling problem in a multi-area power system seeks to
optimize tie-line power flows across areas that are independently operated by
different system operators (SOs). In this paper, we leverage the theory of
multi-parametric linear programming to propose algorithms for optimal tie-line
scheduling within a deterministic and a robust optimization framework. Through
a coordinator, the proposed algorithms are proved to converge to the optimal
schedule within a finite number of iterations. A key feature of the proposed
algorithms, besides their finite step convergence, is the privacy of the
information exchanges; the SO in an area does not need to reveal its dispatch
cost structure, network constraints, or the nature of the uncertainty set to
the coordinator. The performance of the algorithms is evaluated using several
power system examples
Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty
In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed
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Robust optimization for energy transactions in multi-microgrids under uncertainty
Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid
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