[Paper Review] Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

Emil Namor, Fabrizio Sossan+2 more|arXiv (Cornell University)|Mar 2, 2018

Microgrid Control and Optimization33 references147 citations

TL;DR

The paper presents a two-phase control framework that maximizes battery utilization by simultaneously dispatching a medium-voltage feeder and providing primary frequency regulation, validated through simulations and real-scale experiments on a 560 kWh/720 kVA Li-ion BESS.

ABSTRACT

In this paper, we propose a control framework for a battery energy storage system to provide simultaneously multiple services to the electrical grid. The objective is to maximise the battery exploitation from these services in the presence of uncertainty (load, stochastic distributed generation, grid frequency). The framework is structured in two phases. In a period-ahead phase, we solve an optimization problem that allocates the battery power and energy budgets to the different services. In the subsequent real-time phase the control set-points for the deployment of such services are calculated separately and superimposed. The control framework is first formulated in a general way and then casted in the problem of providing dispatchability of a medium voltage feeder in conjunction to primary frequency control. The performance of the proposed framework are validated by simulations and real-scale experi- ments, performed with a grid-connected 560 kWh/720 kVA Li-ion battery energy storage system.

Motivation & Objective

• Motivate and enable simultaneous provision of multiple grid services by a BESS to improve utilization and economics.
• Develop a general scheduling framework that allocates power and energy budgets to multiple services under uncertainty.
• Propose a two-layer control architecture (period-ahead optimization and real-time execution) that is simple to implement.
• Demonstrate applicability to dispatching a MV feeder and providing primary frequency regulation (PFR).
• Provide experimental validation to support real-world deployability of the framework.

Proposed method

• Formulate a generic scheduling problem that allocates service budgets P_j and E_j for J services within a time window T.
• Maximize the widths of the aggregated energy budgets w(Σ_j E_j) subject to E_init+Σ_j E_j ∈ [E_min,E_max] and Σ_j P_j ∈ [-P_max, P_max].
• Define budget trajectories for each service as intervals P_j,k = [P_j,k^down, P_j,k^up] and E_j,k = [E_j,k^down, E_j,k^up].
• Apply a two-tier control: day-ahead optimization to set budgets and a real-time controller that computes additive service setpoints and superimposes them.
• Specialize the generic framework to dispatch the feeders and provide primary frequency regulation, including formulation of dispatch and PFR budgets and the α parameter for PFR.
• Incorporate battery efficiency and conservative energy bounds to reflect non-ideal BESS performance.

Experimental results

Research questions

• RQ1 How can a BESS be scheduled to simultaneously provide multiple grid services while respecting its energy and power constraints under uncertainty?
• RQ2 Can a two-layer control scheme (period-ahead budgeting and real-time superposition) reliably dispatch a MV feeder and provide primary frequency regulation?
• RQ3 What is the impact of stochastic feeder demand and frequency deviations on the exploitable BESS capacity and service provision?
• RQ4 Does the framework scale to real experiments with existing grid resources and achieve practical, measurable performance?

Key findings

• The day-ahead optimization yields an α^o (PFR coefficient) and an offset profile F^o that maximize battery exploitation across a 24-hour horizon.
• Across 31 simulated days, the average α^o is 216.6 kW/Hz, corresponding to up to 43 kW of PFR when Δf_max = 200 mHz.
• Dispatched energy budgets often align with the dispatch service limits, enabling additional capacity to be allocated to PFR depending on forecast uncertainty.
• Experiments on a grid-connected 560 kWh/720 kVA BESS show α^o values of 584 kW/Hz (Day 1) and 127 kW/Hz (Day 2), with mean dispatch offsets of 0.84 kW and -0.56 kW, respectively.
• The RMS tracking error for feeder dispatch is about 0.5 kW over ~130 kW average feeder load, indicating good tracking performance.
• The framework can exploit remaining BESS capacity to provide PFR while maintaining feasible operation and battery health constraints.