Co-optimizing Energy, Grid Services and Voltage Support in Networks with Distributed Storage

This paper presents a framework for co-optimization of energy arbitrage, grid (ancillary) services, and network voltage support subject to real world cyberphysical constraints in an electric distribution network with high DER penetration, specifically distributed storage and solar generation. The framework uses a previously introduced 2-layer architecture, which is appropriately modified to minimize excess voltage deviations and total energy cost while simultaneously maximizing profit from participation in frequency regulation and ramping services. The global controller handles resource scheduling and grid service disaggregation while respecting network constraints. Each local controller attempts to maximize arbitrage profit while following the load profile and grid service schedule dictated by the global controller. Simulations using a benchmark distribution network show that: (i) the performance of the proposed controller is close to that of a perfect foresight controller and better than an opportunistic controller that does not plan for grid service events, (ii) the controller behaves as expected with the price signals, and (iii) it performs effectively across a wide range of solar and storage penetrations and configurations.

[1]  Ratnesh Sharma,et al.  A new co-optimization model for grid scale storage units in energy and frequency regulation markets , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[2]  Abbas El Gamal,et al.  Co-optimizing the value of storage in energy and regulation service markets , 2017 .

[3]  Ram Rajagopal,et al.  Detection and Statistics of Wind Power Ramps , 2013, IEEE Transactions on Power Systems.

[4]  Vincenzo Marano,et al.  A stochastic dynamic programming model for co-optimization of distributed energy storage , 2013, Energy Systems.

[5]  Cesar A. Silva-Monroy,et al.  A comparison of policies on the participation of storage in U.S. frequency regulation markets , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[6]  T. E. McDermott,et al.  Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders , 2018, IEEE Transactions on Power Systems.

[7]  Di Wang,et al.  Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains , 2017, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[8]  Ram Rajagopal,et al.  A Two-Layer Decentralized Control Architecture for DER Coordination , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[9]  Ufuk Topcu,et al.  Exact Convex Relaxation of Optimal Power Flow in Radial Networks , 2013, IEEE Transactions on Automatic Control.