Energy storage technologies and real life applications – A state of the art review

Natural disasters can cause large blackouts. Research into natural disaster impacts on electric power systems is emerging to understand the causes of the blackouts, explore ways to prepare and harden the grid, and increase the resilience of the power grid under such events. At the same time, new technologies such as smart grid, micro grid, and wide area monitoring applications could increase situational awareness as well as enable faster restoration of the system. This paper aims to consolidate and review the progress of the research field towards methods and tools of forecasting natural disaster related power system disturbances, hardening and pre-storm operations, and restoration models. Challenges and future research opportunities are also presented in the paper.

Research on Resilience of Power Systems Under Natural Disasters—A Review

Microgrids with distributed generation (DG) provide a resilient solution in the case of major faults in a distribution system due to natural disasters. This paper proposes a novel distribution system operational approach by forming multiple microgrids energized by DG from the radial distribution system in real-time operations to restore critical loads from the power outage. Specifically, a mixed-integer linear program is formulated to maximize the critical loads to be picked up while satisfying the self-adequacy and operation constraints for the microgrids formation problem by controlling the ON/OFF status of the remotely controlled switch devices and DG. A distributed multiagent coordination scheme is designed via local communications for the global information discovery as inputs of the optimization, which is suitable for autonomous communication requirements after the disastrous event. The formed microgrids can be further utilized for power quality control and can be connected to a larger microgrid before the restoration of the main grids is complete. Numerical results based on modified IEEE distribution test systems validate the effectiveness of our proposed scheme.

Resilient Distribution System by Microgrids Formation After Natural Disasters

Grid-scale energy storage applications in renewable energy integration: A survey

The deployment of energy storage systems (ESSs) is a significant avenue for maximising the energy efficiency of a distribution network, and overall network performance can be enhanced by their optimal placement, sizing, and operation. An optimally sized and placed ESS can facilitate peak energy demand fulfilment, enhance the benefits from the integration of renewables and distributed energy sources, aid power quality management, and reduce distribution network expansion costs. This paper provides an overview of optimal ESS placement, sizing, and operation. It considers a range of grid scenarios, targeted performance objectives, applied strategies, ESS types, and advantages and limitations of the proposed systems and approaches. While batteries are widely used as ESSs in various applications, the detailed comparative analysis of ESS technical characteristics suggests that flywheel energy storage (FES) also warrants consideration in some distribution network scenarios. This research provides recommendations for related requirements or procedures, appropriate ESS selection, smart ESS charging and discharging, ESS sizing, placement and operation, and power quality issues. Furthermore, this study identifies future research opportunities in relation to challenges for optimal ESS placement planning, development and implementation issues, optimisation techniques, social impacts, and energy security.

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Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality

Improved formulations of and solution techniques for the alternating current optimal power flow (ACOPF) problem are critical to improving current market practices in economic dispatch. We introduce the IV-ACOPF formulation that unlike canonical ACOPF formulations–which represent network balancing through nonlinear coupling–is based on a current injections approach that linearly couple the quadratic constraints at each bus; yet, the IV-ACOPF is mathematically equivalent to the canonical ACOPF formulation. We propose a successive linear programming (SLP) approach to solve the IV-ACOPF, which we refer to as the SLP IV-ACOPF algorithm. The SLP IV-ACOPF leverages commercial LP solvers and can be readily extended and integrated into more complex decision processes, e.g., unit commitment and transmission switching. We demonstrate with the standard MATPOWER test suite an acceptable quality of convergence to a best-known solution and linear scaling of computational time in proportion to network size.

A Successive Linear Programming Approach to Solving the IV-ACOPF

Risk analysis and management in power outage and restoration: A literature survey

We propose a mathematical programming-based approach to optimize the unit commitment problem with alternating current optimal power flow (ACOPF) network constraints. This problem is a nonconvex mixed-integer nonlinear program (MINLP) that we solve through a solution technique based on the outer approximation method. Our solution technique cooptimizes real and reactive power scheduling and dispatch subject to both unit commitment constraints and ACOPF constraints. The proposed approach is a local solution method that leverages powerful linear and mixed-integer commercial solvers. We demonstrate the relative economic and operational impact of more accurate ACOPF constraint modeling on the unit commitment problem, when compared with copperplate and DCOPF constraint modeling approaches; we use a six-bus, the IEEE RTS-79, and the IEEE-118 test systems for this analysis. Our approach can be extended to solve larger scale power systems as well as include security constraints or uncertainty through decomposition techniques.

The Unit Commitment Problem With AC Optimal Power Flow Constraints

This work investigates the interaction between nodal price signals and the optimal allocation and operation of distributed energy storage systems (ESS) in alternating current (AC) power networks. We model a multi-period optimal power flow (OPF) problem with charge and discharge dynamics for energy storage collocated with load and/or generation. We then apply a convex relaxation based on semidefinite programming (SDP) and derive the storage subproblem from the Lagrangian dual. We use the storage subproblem to investigate the relationship between the storage variables and the locational marginal prices (LMPs), which are market-based price signals defined by the dual variables associated with the nodal active power flow balance. Our theoretical results prove that LMPs drive charging and discharging dynamics, and that storage is allocated and operated to maximize the storage operator's profits, i.e. minimize the system costs in a purely competitive market. We then use this framework to illustrate LMP-based storage dynamics.

Anya Castillo

Papers

Grid-scale energy storage applications in renewable energy integration: A survey

A Successive Linear Programming Approach to Solving the IV-ACOPF

Risk analysis and management in power outage and restoration: A literature survey

The Unit Commitment Problem With AC Optimal Power Flow Constraints

Profit maximizing storage allocation in power grids