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1. Determining Reserve Requirements for Energy Storage to Manage Demand-Supply Imbalance in Power Grids

   Parker, Kendall; Barooah, Prabir (October 2018, 2018 IEEE Electronic Power Grid (eGrid))

   Proper integration of energy storage systems (ESS) into existing or future grids will depend on the effectiveness of models which seek optimal placement and sizing at the transmission and distribution levels. Current literature reviews reveal sizing methodologies can be improved to ease infrastructure integration, and those works with models useful for planning focus solely on micro-grids, wind power and forecasting, photovoltaics, or small communities. It is of interest to create an efficient, reliable ESS sizing model for large scale grids that contains interpretable models, has less sensitivity due to low model uncertainty, yet still is dependable due to an imposed reliability criterion. This work determined the minimum feasible size ESS to satisfy reserve requirements for a power grid with a high penetration of renewable sources. Results showed imposing a reliability criterion through loss of load expectation (LOLE) and energy index of reliability (EIR) resulted in more conservative capacity needs. 

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2. Exploring reactive power limits on wind farm collector networks with convex inner approximations

   Nazir, Nawaf; Hiskens, Ian; Almassalkhi, Mads (July 2022, 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM)

   A wind farm can provide reactive power at sub-transmission and transmission buses in order to support and improve voltage profiles. It is common for the reactive power capability of a wind farm to be evaluated as the sum of the individual turbine ratings. However, such an assessment does not take into account losses over the collector network, nor the voltage constraints imposed by the turbines and network. In contrast, the paper presents a method for determining the range of reactive power support that each turbine can provide whilst guaranteeing satisfaction of voltage constraints. This is achieved by constructing convex inner approximations of the non-convex set of admissible reactive power injections. We present theoretical analysis that supports the constraint satisfaction guarantees. An example illustrates the effectiveness of the algorithm and provides a comparison with a fully decentralized approach to controlling wind farm reactive power. Such approaches have the potential to improve the design and operation of wind farm collector networks, reducing the need for additional costly reactive power resources. 

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3. A Model Predictive Approach for Voltage Support in Microgrids using Energy Storage Systems

   https://doi.org/10.1109/PESGM46819.2021.9638037  

   Bhujel, Niranjan; Rai, Astha; Hansen, Timothy M.; Tonkoski, Reinaldo; Tamrakar, Ujjwol (January 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   Low voltage microgrid systems are characterized by high sensitivity to both active and reactive power for voltage support. Also, the operational conditions of microgrids connected to active distribution systems are time-varying. Thus, the ideal controller to provide voltage support must be flexible enough to handle technical and operational constraints. This paper proposes a model predictive control (MPC) approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system along with operational constraints to solve an online finite-horizon optimization problem. Control signals are then computed such that the defined cost function is minimized. By proper selection of MPC weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in Matlab/Simulink validates the proposed approach for a simplified version of a 100 kVA, 208 V microgrid using typical parameters. Results show that performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller. 

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4. Adaptive Deep Reinforcement Learning for VVC in High DER-Integrated Distribution Grids

   https://doi.org/10.1109/TPEC63981.2025.10906926  

   Vallam_Kondu, Sai Chandana; Ravikumar, Gelli (February 2025, IEEE)

   The increasing penetration of Distributed Energy Resources (DERs) in modern distribution grids has created sig-nificant challenges in maintaining voltage profiles and minimizing reactive power losses. While Deep Reinforcement Learning (DRL) approaches have been explored for Volt/VAR Control (VVC), existing methods often rely on single-agent frameworks or lack adaptive mechanisms to balance competing grid objectives dy-namically. Multi-Agent (MA) DRL frameworks have emerged but typically require explicit inter-agent communication, limiting scalability and practicality. To address these limitations, we propose a novel MA-DRL framework that incorporates adaptive reward computation to implicitly coordinate special-ized agents, including a Voltage Control Agent for maintaining voltage profiles and a Reactive Power Agent for optimizing reactive power dispatch. Unlike prior approaches, our method dynamically adjusts local and global rewards based on real-time grid conditions, enabling scalable and efficient grid control without explicit inter-agent communication. We evaluated the framework on the IEEE 123-bus test system under high DER penetration. The results demonstrated that the proposed MA-DRL approach successfully eliminated voltage violations, reduced power losses through optimized reactive power dispatch, and decreased transformer tap changes and capacitor operations, thereby extending equipment lifespan. These results demonstrate the scalability and practicality of adaptive reward-based MA-DRL for addressing modern grid management challenges. 

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5. Joint sizing and placement of battery energy storage systems and wind turbines considering reactive power support of the system

   Khaki, Bahman (January 2021, Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   A B S T R A C T The probabilistic and intermittent output power of Wind Turbines (WT) is one major inconsistency of these Renewable Energy Sources (RES). Battery Energy Storage Systems (BESS) are a suitable solution to mitigate this intermittency by smoothening WT’s output power. Although the main benefit of BESSs mentions as peak shaving and load-shifting, but in this research, it will verify that optimal placement and sizing them jointly with WTs can lead to more benefits like compensating the required system’s reactive power support from WTs. The reactive power size of WTs and BESSs will be derived from the result of the joint sizing and placement in this study, as well as their active power output to meet the load demand. This can facilitate WTs and BESSs contribution to cover the system’s required reactive power and their participation in the reactive power market and ancillary services. This paper also proposes new cost functions for both WTs and BESSs and minimizes their cost while ensuring minimal total loss (active and reactive) in the power distribution system. This can benefit both WTs’ and BESSs’ owners as well as system operators. Suitable placement and sizing of the WTs and BESSs can also improve the load bus voltage profiles, which can benefit the end-users, and will verify using the proposed optimization by different case studies on the 33 bus distribution system. The results of case studies ascertain the consistency of the proposed formulation for placement and sizing BESSs and WTs jointly, as well as other benefits to the power system, the power plant owners, and system operators. 

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