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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. [ABSTRACT FROM AUTHOR]

To ramp-up the utilization of wind energy, the uncertainty of wind power needs to be properly addressed in its integration into the power grid. The main challenge is to well tackle the risk induced by the uncertain wind power generation in different operation stages of a wind farm. This paper studies a novel bi-level multi-timescale scheduling approach for accommodating the wind power uncertainty via a robust optimization formulation. Wind farm day-ahead and intra-day operations consider different scheduling timescales. A two-stage robust optimization model is developed to plan the day-ahead power generation commitments to the power grid. A multi-stage robust optimization model is proposed to refine intra-day wind farm operation instructions. The overall decision-making process aims to minimize the wind farm operational cost. The proposed models are solved by using computationally tractable optimization methods, which offer a desired performance guarantee. A comprehensive case study based on real datasets to demonstrate the effectiveness and robustness of the proposed approach is carried out. Average cost reductions of 46.648% and 6.883% are achieved by comparing with the deterministic method and the static robust method, respectively. Results verify that the proposed method can provide optimal and robust wind farm operations schedules with a lower cost, higher flexibility, and less conservativeness. Results also demonstrate the feasibility of applying the proposed approach to the real-world wind farm and providing reliable operation guidelines. [ABSTRACT FROM AUTHOR]

• Simultaneously scheduling power productions and maintenance activities of an offshore wind farm is studied. • A two-stage model is developed to offer different time-scale schedule of power productions and maintenance. • Uncertainty of wind power and tide conditions on scheduling is considered. • Robust schedules of the two-stage model tackle uncertainties while optimize operations and maintenance. This paper studies the model for scheduling both offshore wind farm maintenance tasks and power productions in consideration of their distinct scheduling timescales and the wind power uncertainty. A two-stage adaptive robust optimization model is formulated to derive the optimal schedule, which well handles wind turbine maintenance tasks and maximizes wind farm power productions by anticipating the worst wind power output scenario based on a wind power uncertainty set and a set of metocean conditions. In two decision stages, we schedule maintenance tasks daily and power productions hourly respectively. The proposed model is equivalently transformed to a form which can be efficiently solved by the column-and-constraint generation algorithm. Numerical experiments demonstrate the effectiveness and applicability of the proposed model considering wind farms of different large sizes. Results show that the developed model can maximize the total power production of the wind farm and enhance the robustness of obtained maintenance schedules against the wind power uncertainty. [ABSTRACT FROM AUTHOR]

• Solved the energy dispatch problem for multi-stakeholder MMGs under uncertainty. • Proposed a robust distributed optimization based MMGs energy dispatch approach. • Designed a hierarchical dispatch framework to aggregate and coordinate energy trading. • Mitigated the disturbances of uncertainties in distributed decision making of stakeholders. • Realized parallel computing for energy dispatch optimization problems for all stakeholders. This paper addresses the energy dispatch problem for multi-stakeholder multiple microgrids (MMGs) under uncertainty while considering independent market operators (IMOs) based energy trading forms. Firstly, a collaborative hierarchical dispatch framework is proposed to adapt to decentralized multiple stakeholders and coordinate energy trading between IMOs and microgrids (MGs). And then this framework is further decomposed into different independent optimization problems for stakeholders based on an analytical target cascading (ATC) algorithm, in which Lagrangian penalty terms are introduced to ensure consistency in energy trading. In these optimization problems, energy trading and production of an individual MG is formulated as a two-stage adaptive robust optimization model to hedge against uncertainties from random renewable energy sources and loads. Moreover, in order to realize parallel computing for all independent optimization problems, a diagonal quadratic approximation method is applied to linearize quadratic terms. We integrate the ATC algorithm with a column-and-constraint generation algorithm to derive robust energy dispatch schemes in parallel. Finally, simulations on different cases are conducted to testify the rationality and validity of the proposed robust distributed energy dispatch approach. The results show that the hierarchical energy dispatch framework with IMOs has advantages over that without IMOs. Moreover, the proposed approach can reduce the impacts of uncertainties on distributed decision making of multiple stakeholder and enhance the computational efficiency. [ABSTRACT FROM AUTHOR]