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An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers' privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids. [ABSTRACT FROM AUTHOR]

In this study, a distributed energy management for community microgrids considering phase balancing and peak shaving is proposed. In each iteration, the house energy management system (HEMS) installed in each house minimises its electricity costs and the costs associated with the discomfort of customers due to deviations in indoor temperature from customers' set points. At the community level, the microgrid central controller (MCC) schedules the distributed energy resources (DERs) and energy storage based on the received load profiles from customers and the forecast energy price at the point of common coupling. The MCC updates the energy price for each phase based on the amount of unbalanced power between generation and consumption. The updated energy price and unbalanced power for each phase are distributed to the HEMSs on corresponding phases. When the optimisation converges, the unbalanced power of each phase is close to zero. Meanwhile, the schedules of DERs, energy storage systems and the energy consumption of each house are determined by the MCC and HEMSs, separately. In particular, the phase balancing and peak shaving are considered in the proposed distributed energy management model. The effectiveness of the proposed distributed energy management has been demonstrated by case studies. [ABSTRACT FROM AUTHOR]

This study proposes a robust optimisation‐based optimal scheduling model for microgrid operation considering constraints of islanding capability. The objective is to minimise the total operation cost, including generation cost and spinning reserve cost of local resources as well as purchasing cost of energy from the main grid. To ensure the resiliency of a microgrid and improve the reliability of the local electricity supply, the microgrid is required to maintain enough spinning reserve (both up and down) to meet local demand and accommodate local renewable generation when the supply of power from the main grid is interrupted suddenly, i.e. microgrid transitions from grid‐connected into islanded mode. Prevailing operational uncertainties in renewable energy resources and load are considered and captured using a robust optimisation method. With proper robust level, the solution of the proposed scheduling model ensures successful islanding of the microgrid with minimum load curtailment and guarantees robustness against all possible realisations of the modelled operational uncertainties. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro‐turbine, a diesel generator and a battery demonstrate the effectiveness of the proposed scheduling model. [ABSTRACT FROM AUTHOR]

To allow for high penetration of distributed generation and alternative energy units, it is critical to minimize the complexity of generator controls and the need for close coordination across regions. We propose that existing controls be replaced by a two-tier structure of local control operating within a global context of situational awareness. Flatness as an extension of controllability for nonlinear systems is a key to enable planning and optimization at various levels of the grid in this structure. In this study, flatness-based control for automatic generation control (AGC) of a multi-machine system with high penetration of wind energy is investigated. The local control tracks the reference phase which is obtained through economic dispatch at the global control level. As a result of applying the flatness-based method, the n machine system is decoupled into n linear controllable systems in canonical form. Therefore, the control strategy results in a distributed AGC formulation which is significantly easier to design and implement compared to conventional AGC. Practical constraints such as generator ramping rates can be considered in designing the local controllers. The proposed strategy demonstrates promising performance in mitigating frequency deviations and the overall structure could facilitate operation of other nontraditional generators. [ABSTRACT FROM PUBLISHER]