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1. Analytical Study Based Optimal Placement of Energy Storage Devices in Power Systems to Support Voltage and Angle Stability

   Mehdy Khayamy, Adel Nasiri ( December 2019 , International journal of smart grids)

   Larger penetration of Distributed Generations (DG) in the power system brings new flexibility and opportunity as well as new challenges due to the generally intermittent nature of DG. When these DG are installed in the medium voltage distribution systems as components of the smart grid, further support is required to ensure a smooth and controllable operation. To complement the uncontrollable output power of these resources, energy storage devices need to be incorporated to absorb excessive power and provide power shortage in time of need. They also can provide reactive power to dynamically help the voltage profile. Energy Storage Systems (ESS)more »can be expensive and limited number of them can practically be installed in distribution systems. In addition to frequency regulation and energy time shifting, ESS can support voltage and angle stability in the power network. This paper applies a Jacobian matrix-based sensitivity analysis to determine the most appropriate node in a grid to collectively improve the voltage magnitude and angle of all the nodes by active/reactive power injection. IEEE 14, 24, and 123-bus distribution system are selected to demonstrate the performance of the proposed method. As opposed to most previous studies, this method does not require an iteration loop with a convergence problem nor a network-related complicated objective function.« less
2. Coordinated Electric Vehicle Charging with Reactive Power Support to Distribution Grids

   https://doi.org/10.1109/TII.2018.2829710  

   Wang, Jingyuan ; Bharati, Guna Ratna ; Paudyal, Sumit ; Ceylan, Oguzhan ; Bhattarai, Bishnu Prasad ; Myers, Kurt S. ( January 2019 , IEEE Transactions on Industrial Informatics)

   We develop hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed electric vehicles (EVs) incorporating distribution grid level constraints. The frameworks consist of detailed mathematical models, which can benefit the operation of both entities involved, i.e., the grid operations and EV charging. The first model comprises of a comprehensive optimal power flow model at the distribution grid level, while the second model represents detailed optimal EV charging with reactive power support to the grid. We demonstrate benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder withmore »large number of EVs (more than 5,000). Case studies demonstrate that, in constrained distribution grids, coordinated charging reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to unity power factor.« less
3. Deep Learning Based Multi-Label Attack Detection for Distributed Control of AC Microgrids

   https://doi.org/10.1109/SmartGridComm51999.2021.9631998  

   Mohiuddin, Sheik M. ; Qi, Junjian ; Fung, Sasha ; Huang, Yu ; Tang, Yufei ( October 2021 , 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   This paper presents a deep learning based multi-label attack detection approach for the distributed control in AC microgrids. The secondary control of AC microgrids is formulated as a constrained optimization problem with voltage and frequency as control variables which is then solved using a distributed primal-dual gradient algorithm. The normally distributed false data injection (FDI) attacks against the proposed distributed control are then designed for the distributed gener-ator's output voltage and active/reactive power measurements. In order to detect the presence of false measurements, a deep learning based attack detection strategy is further developed. The proposed attack detection is formulated asmore »a multi-label classification problem to capture the inconsistency and co-occurrence dependencies in the power flow measurements due to the presence of FDI attacks. With this multi-label classification scheme, a single model is able to identify the presence of different attacks and load change simultaneously. Two different deep learning techniques are compared to design the attack detector, and the performance of the proposed distributed control and the attack detector is demonstrated through simulations on the modified IEEE 34-bus distribution test system.« less
4. Coordinating Distribution System Resources for Co-optimized Participation in Energy and Ancillary Service Transmission System Markets

   https://doi.org/10.23919/ACC.2019.8815242  

   Ji, Chengda ; Hajiesmaili, Mohammad ; Gayme, Dennice F. ; Mallada, Enrique ( July 2019 , American Control Conference)

   his work investigates the potential of using aggregate controllable loads and energy storage systems from multiple heterogeneous feeders to jointly optimize a utility's energy procurement cost from the real-time market and their revenue from ancillary service markets. Toward this, we formulate an optimization problem that co-optimizes real-time and energy reserve markets based on real-time and ancillary service market prices, along with available solar power, storage and demand data from each of the feeders within a single distribution network. The optimization, which includes all network system constraints, provides real/reactive power and energy storage set-points for each feeder as well as amore »schedule for the aggregate system's participation in the two types of markets. We evaluate the performance of our algorithm using several trace-driven simulations based on a real-world circuit of a New Jersey utility. The results demonstrate that active participation through controllable loads and storage significantly reduces the utility's net costs, i.e., real-time energy procurement costs minus ancillary market revenues.« less
5. An integrated approach to microgrid sizing and flow shop scheduling via climate analytics

   Subramanyam, V. ; Jin, T. ; Novoa, C. ( April 2020 , Journal of cleaner production)

   A variety of methods have been proposed to assist the integration of microgrid in flow shop systems with the goal of attaining eco-friendly operations. There is still a lack of integrated planning models in which renewable portfolio, microgrid capacity and production plan are jointly optimized under power demand and generation uncertainty. This paper aims to develop a two-stage, mixed-integer programming model to minimize the levelized cost of energy of a flow shop powered by onsite renewables. The first stage minimizes the annual energy use subject to a job throughput requirement. The second stage aims at sizing wind turbine, solar panelsmore »and battery units to meet the hourly electricity needs during a year. Climate analytics are employed to characterize the stochastic wind and solar capacity factor on an hourly basis. The model is tested in four locations with a wide range of climate conditions. Three managerial insights are derived from the numerical experiments. First, time-of-use tariff significantly stimulates the wind penetration in locations with medium or low wind speed. Second, regardless of the climate conditions, large-scale battery storage units are preferred under time-of-use rate but it is not the case under a net metering policy. Third, wind- and solar-based microgrid is scalable and capable of meeting short-term demand variation and long-term load growth with a stable energy cost rate.« less