Performance studies and energy management of aggregated BESSs for frequency regulation
Date of Issue2017-07-21
School of Electrical and Electronic Engineering
With the increasing installed capacity of renewable energy sources (RESs) in the power systems, their uncertainty and intermittency are causing power imbalances in the grid, which leads to frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation resources to recover the frequency within a short period of time. While traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signals, battery energy storage systems (BESSs) are considered as an effective regulation source to respond immediately to frequency deviations. From the system operator's point of view, a proper procedure for BESS sizing and control is required to ensure a long-term high quality regulation service on the system level. We study the sizing issue of a BESS aggregation based on the overall system control performance. The frequency performance with different penetration rates of BESS is analyzed. The frequency performance is evaluated by Control Performance Standard 1 (CPS1) and Control Performance Standard 2 (CPS2) proposed by North American Electricity Reliability Corporation (NERC). The recommended BESS penetration rate in the power system is analyzed using an iterative approach according to the NERC performance indices. Taking the investment cost into consideration, a 5% to 15% BESS penetration rate is recommended for the test system. For a network penetrated with dispersedly located RESs, distributed energy resources (DERs) and storage devices, the virtual power plant (VPP) concept is applied to aggregate the resources. A centralized hierarchical controller is proposed for the VPP to achieve a more economical operation and more effective system frequency regulation. Model predictive control (MPC) strategy is implemented in the controller. The VPP's influence on system frequency is studied by real-time simulations. The results show that the secondary control executes economic dispatch to coordinate the power dispatch within the VPP according to photovoltaic (PV) power output, load data, and real-time electricity tariff. In the meantime, the primary controller is capable of stabilizing system frequency within the permitted range during heavy load and peak solar generation periods. The proposed hierarchical control scheme is further developed for a distribution network with distributed BESSs which can be aggregated as a VPP. As it is difficult for BESSs to be profitable due to high battery costs, the aggregated BESSs are maneuvered to participate in multiple markets. We propose a hierarchical energy management system (HiEMS) for aggregated BESSs taking part in both energy and regulation markets. The HiEMS performs optimal scheduling in multiple markets and attempts to coordinate BESSs of different battery types, various state of charge (SOC), and power and energy capacity. The SOC values are regulated around the expected average SOC to prevent individual saturation or depletion, and thus increasing the average battery lifetime. The proposed HiEMS can support up to 0.5 regulation participation rate, which will boost the cost performance index (CPI) by 7 times. HiEMS outperforms participation factor (PF) method by 1.24 times and master-slave (M-S) method by 1.4. In terms of time of first replacement, HiEMS is 2.15 times loner than that of the PF method and 3.75 times longer than that of the MS method. In current markets, the performance-based regulation market in Pennsylvania-Jersey-Maryland power pool, i.e. PJM Interconnection Company, is the most profitable one for BESSs. While the BESSs are always assumed to deliver very high performance, an optimal schedule optimizer is proposed including an innovative realistic BESS performance model. The performance model, which characterizes the trade-off between the performance and the power bids in both energy and regulation markets, contributes to the regulation energy scheduling and guarantees BESS performance. The schedule optimizer also models the pricing uncertainties in both markets by generating scenarios according to their respective statistical characteristics. After adopting the proposed optimal scheduling strategy, the cost-performance index (CPI) of the BESSs governed by HiEMS is boosted to 31.70%, compared to around 10% from only energy market participation.