Chunlian Jin

Sizing Energy Storage to Accommodate High Penetration of Variable Energy Resources

The variability and nondispatchable nature of wind and solar energy production presents substantial challenges for maintaining system balance. Depending on the economic considerations, energy storage can be a viable solution to balance energy production with consumption. This paper proposes to use discrete Fourier transform to decompose the required balancing power into different time-varying periodic components, i.e., intraweek, intraday, intrahour, and real-time. Each component can be used to quantify the maximum energy storage requirement for different types of energy storage. This requirement is the physical limit that could be theoretically accommodated by a power system. The actual energy storage capacity can be further quantified within this limit by the cost-benefit analysis (future work). The proposed approach has been successfully used in a study conducted for the 2030 Western Electricity Coordinating Council system model. Some results of this study are provided in this paper.

Centralized and decentralized control for demand response

Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generators are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their response performances in terms of delay time and predictability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the controllability and predictability of centralized control to achieve the best performance of the smart grid.

Energy Storage for Power Systems Applications: A Regional Assessment for the Northwest Power Pool (NWPP)

Wind production, which has expanded rapidly in recent years, could be an important element in the future efficient management of the electric power system; however, wind energy generation is uncontrollable and intermittent in nature. Thus, while wind power represents a significant opportunity to the Bonneville Power Administration (BPA), integrating high levels of wind resources into the power system will bring great challenges to generation scheduling and in the provision of ancillary services. This report addresses several key questions in the broader discussion on the integration of renewable energy resources in the Pacific Northwest power grid. More specifically, it addresses the following questions: a) how much total reserve or balancing requirements are necessary to accommodate the simulated expansion of intermittent renewable energy resources during the 2019 time horizon, and b) what are the most cost effective technological solutions for meeting load balancing requirements in the Northwest Power Pool (NWPP).

National Assessment of Energy Storage for Grid Balancing and Arbitrage: Phase 1, WECC

To examine the role that energy storage could play in mitigating the impacts of the stochastic variability of wind generation on regional grid operation, the Pacific Northwest National Laboratory (PNNL) examined a hypothetical 2020 grid scenario in which additional wind generation capacity is built to meet renewable portfolio standard targets in the Western Interconnection. PNNL developed a stochastic model for estimating the balancing requirements using historical wind statistics and forecasting error, a detailed engineering model to analyze the dispatch of energy storage and fast-ramping generation devices for estimating size requirements of energy storage and generation systems for meeting new balancing requirements, and financial models for estimating the life-cycle cost of storage and generation systems in addressing the future balancing requirements for sub-regions in the Western Interconnection. Evaluated technologies include combustion turbines, sodium sulfur (Na-S) batteries, lithium ion batteries, pumped-hydro energy storage, compressed air energy storage, flywheels, redox flow batteries, and demand response. Distinct power and energy capacity requirements were estimated for each technology option, and battery size was optimized to minimize costs. Modeling results indicate that in a future power grid with high-penetration of renewables, the most cost competitive technologies for meeting balancing requirements include Na-S batteries and flywheels.

Control and Size Energy Storage Systems for Managing Energy Imbalance of Variable Generation Resources

This paper presents control algorithms and sizing strategies for using energy storage to manage energy imbalance for variable generation resources. The control objective is to minimize the hourly generation imbalance between the actual and the scheduled generation of wind farms. Three control algorithms are compared: 1)tracking minute-by-minute power imbalance; 2)postcompensation; and 3)precompensation. Measured data from a wind farm are used in the study. The results show that tracking minute-by-minute power imbalance achieves the best performance by keeping hourly energy imbalance zero. However, the energy storage system (ESS) will be significantly oversized. Postcompensation reduces the power rating of the ESS but the hourly energy imbalance may not be reduced to zero when a large and long-lasting power imbalance occurs. A linear regression forecasting algorithm is developed for a two-stage precompensation algorithm to precharge or predischarge the ESS based on the predicted energy imbalance. An equivalent charge cycle estimation method is proposed to evaluate the effect of providing the energy balancing service on battery life. The performance comparison shows that the precompensation method reduces the size of the ESS by 30% with satisfactory performance.

Coordinated control algorithm for hybrid energy storage systems

Energy storage is an essential element of future power systems to integrate high level of variable renewable energy resources. Earlier studies have found that energy storage can compensate for the stochastic nature of intermittent energy sources by absorbing the excessive energy when generation exceeds predicted levels and providing it back to the grid when generation levels fall short. However, earlier economic studies have shown that battery energy storage and flywheel energy storage is not economically competitive comparing to traditional generation units. An optimal control algorithm has been developed to coordinate the slow unit (having respond time greater than 1 minute) and fast energy storage unit (having response time less than 1 minute) to maximize the revenue (or minimize the total cost) of the hybrid energy storage system. The fast energy storage unit, (which can be a flywheel or battery bank) is tuned to pick up the fluctuations of regulation signal while the slow unit, (which can be a traditional generation unit or slow energy storage system) is adjusted less than once per hour to provide regulation service. Simulation models of hydro, combined cycle, and flywheel unit have been developed and implemented in MATLAB. Extensive simulations demonstrate the effectiveness of the control algorithm. The value of the algorithm has been shown from power plant wear and tear aspect and reducing system balancing reserve aspect.

Optimal size of energy storage to accommodate high penetration of renewable resources in WECC system

The variability and intermittence of wind power will cause the large imbalance power that demands more expensive ancillary service. Energy storage, fast response but costly, is a viable solution to suppress the fluctuation of wind power. However, the determination of energy storage is a great challenge given the load demand and wind power uncertainties This paper proposes to use discrete fourier transform (DFT) to decompose the imbalance power into different time-varying components, i.e., intra-week, intra-day, intra-hour and real-time. Therefore, the imbalance power to be compensated by energy storage can be quantified. By compensating the fast-changing imbalance power (the slowly changing power is provided by the conventional generators), energy storage can be optimized to accommodate integration of high penetration wind power. The simulation results on the 2030 Western Electricity Coordinating Council (WECC) system demonstrate effectiveness and efficiency of this approach.

Energy storage for variable renewable energy resource integration — A regional assessment for the Northwest Power Pool (NWPP)

This paper addresses the following key questions in the discussion on the integration of renewable energy resources in the Pacific Northwest power grid: a) what will be the future balancing requirement to accommodate a simulated expansion of wind energy resources from 3.3 GW in 2008 to 14.4 GW in 2019 in the Northwest Power Pool (NWPP), and b) what are the most cost effective technological solutions for meeting the balancing requirements in the Northwest Power Pool (NWPP). A life-cycle analysis was performed to assess the least-cost technology option for meeting the new balancing requirement. The technologies considered in this study include conventional turbines (CT), sodium sulfur (NaS) batteries, lithium ion (Li-ion) batteries, pumped hydro energy storage (PH), and demand response (DR). Hybrid concepts that combine 2 or more of the technologies above are also evaluated. This analysis was performed with collaboration by the Bonneville Power Administration and funded by the Energy Storage Systems Program of the U.S. Department of Energy.

A Coordinating Algorithm for Dispatching Regulation Services Between Slow and Fast Power Regulating Resources

This paper presents a novel coordinating algorithm for dispatching regulation services between slow and fast power regulating resources using a conventional power generator and a flywheel energy storage system as an example. The goal is to let the flywheel storage device follow the fast changes in the regulation signal and let the conventional generator compensate for the energy imbalance when the flywheel storage is nearly fully charged or discharged. A state-of-charge (SOC) band control algorithm is developed to maintain the storage device SOC within a desired range. Real system regulation signals were used to test the performance of the coordinating algorithm. The simulation results show that: 1) the HRR achieves the same fast response rate as that of the storage device, 2) the up and down movements of the generator are minimized, and 3) the SOC of the storage device is maintained within the desired range most of the time. Therefore, the proposed coordinating algorithm can provide the high quality regulation service while reducing maintenance-inducing strain on conventional generators.

Cross-market optimization for hybrid energy storage systems

A method is developed to generate optimal bid schedules for a hybrid energy storage system participating in both energy and regulation service markets. The hybrid energy storage system includes a fast-response component, such as a flywheel or battery, and a slow response component, such as a pumped-hydro or a conventional generator. This paper describes the objective function and constraints of the cross-market optimization problem. A genetic algorithm is used to solve the problem with a nonlinear penalty curve applied to the energy constraints. A single market optimization method based on priority search is used as a baseline. The results show that the cross-market optimization can improve revenue of the energy storage system by 6.9% over the baseline. Although the method was applied to a hybrid energy storage system, it can be generalized to any energy storage systems.