Interconnecting industrial multi-microgrids using bidirectional hybrid energy links

Sharing and exchange energy among nearby industrial microgrids are crucial, especially with high energy requirements for their production targets and costly energy storage systems that may be oversized for their operations. Facilitating energy exchange can provide an economic advantage for industrial production by utilizing cheaper energy sources and reducing production costs. This manuscript presents an efficient approach for transferring large energy packets with minimal energy losses using high-voltage direct current (HVDC) energy transmission. The manuscript methodology focuses on implementing an industrial multi-microgrid using a modular multilevel converter. This converter utilizes two power link channels: a three-phase AC and an HVDC link, creating a hybrid energy transmission between microgrids. When a substantial amount of energy to transfer, the HVDC method enhances overall efficiency by reducing copper losses and mitigating issues associated with the AC link, such as harmonics and skin effects. The modular multilevel converter topology offers high flexibility and the use of fewer converters. Additionally, the HVDC link eliminates distance restrictions for energy transfer between industrial microgrids. A case study illustrates the functionality of this topology, demonstrating optimized power transfer and decreased energy losses. This methodology allows industrial microgrids to enhance energy efficiency and productivity while minimizing operational costs

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

The coordinated voltage control meets imperfect communication system.

High penetration of Distributed Generations (DGs) will have impact on the development of power systems. Due to the uncertainty of the DG output, it becomes extremely difficult to control the system voltage profile. This paper proposes a coordinated decentralized voltage control method, together with a self-excited inverter, that can control the voltage level by reactive power injection/absorption. The time-delay introduced by communications among DGs is considered to validate the proposed control approach. Simulation results show that the coodinated control approach is sensitive to the time-delay in a 33-bus medium-voltage distribution network (MVDN)

Decentralized Energy Management Concept for Urban Charging Hubs with Multiple V2G Aggregators

This work introduces a decentralized management concept for the urban charging hubs (UCHs) where electric vehicles (EVs) can access multiple charger clusters, each controlled by an aggregator. The given day ahead schedules (DASs) and peak power limits (PPLs) of the aggregators providing grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services can constrain the energy supply. A suitable energy management concept is required to prevent the impact of supply limitations on EV users. In the proposed approach, an electromobility operator (EMO) acting as an authorized entity, allocates incoming EVs into the charger clusters in the UCH. The EMO executes a smart routing (SR) algorithm that jointly optimizes the cluster allocations and charging schedules, minimizing the charging cost for the given dynamic price signals produced by the aggregators. For real-time charging control (RTC) of the charging units, each aggregator solves an optimization problem with periodically updated parameters given by the DAS/PPLs and charging commitments. This work demonstrates the effectiveness of the proposed concept through comparisons against benchmark strategies without SR and RTC. The results highlight that the proposed concept reduces the deviations from the DASs and the violations of PPLs while significantly decreasing unfulfilled charging demand and unscheduled discharge from EV batteries.Decentralized Energy Management Concept for Urban Charging Hubs with Multiple V2G AggregatorsacceptedVersio

Incorporating user utility in a smart microgrid with distributed generation and elastic demand.

Demand Side Management (DSM) will play a large role in creating a pathway to a low carbon future. Microgrids are an ideal test bed for DSM within the Smart Grid (SG) framework, allowing for increased integration of distributed generation (DG), here focused on distributed Renewable Energy Sources (RESs). Existing work uses conservative estimates to model the stochastic nature of RESs, resulting in inaccuracies in simulation results. Large uncertainty in user specific participation in DSM programs exists. This paper develops a flexible energy load function, effectively incorporating different user's behaviour patterns into the DSM framework. Uncertainty in connecting small-scale wind generation into the smart microgrid is reduced by using an expected cost function to accurately map predicted wind speed to power output. Actual wind speed is varied across numerous sub-horizons within each time slot by using a pseudo-random number generator. The stochastic nature of renewable generation is effectively managed, producing a robust simulation. Model sensitivities are investigated and graphical results presented