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Register a new userOptimizing Partial Power Processing for Second-Use Battery Energy Storage Systems
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Repurposing automotive batteries to second-use battery energy storage systems (2-BESS) may have environmental and economic benefits. The challenge with second-use batteries is the uncertainty and diversity of the expected packs in terms of their chemistry, capacity and remaining useful life. This paper introduces a new strategy to optimize 2-BESS performance despite the diversity or heterogeneity of individual batteries while reducing the cost of power conversion. In this paper, the statistical distribution of the power heterogeneity in the supply of batteries is considered when optimizing the choice of power converters and designing the power flow within the battery energy storage system (BESS) to maximize battery utilization. By leveraging a new lite-sparse hierarchical partial power processing (LS-HiPPP) approach, we show a hierarchy in partial power processing (PPP) partitions power converters to a) significantly reduce converter ratings, b) process less power to achieve high system efficiency with lower cost (lower efficiency) converters, and c) take advantage of economies of scale by requiring only a minimal number of sets of identical converters. The results demonstrate that LS-HiPPP architectures offer the best tradeoff between battery utilization and converter cost and had higher system efficiency than conventional partial power processing (C-PPP) in all cases.
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Decentralized renewable energy systems can be low-carbon power sources, and promoters of local economies. It is often argued that decentralized generation also helps reducing transmission costs, as generation is closer to the load, thus utilizing the transmission system less. The research presented here addresses the question whether or not, or under what circumstances this effect of avoided transmission can actually be seen for a community-operated cluster of photovoltaic (PV) power plants in two sample locations, one in Germany and one in Japan. For the analysis, the newly developed instrument of MPI-MPE diagrams is used, which plot the maximum power import (MPI) and maximum power export (MPE) in relation to the reference case of no local generation. Results reveal that for moderately sized PV systems without battery storage, avoided transmission can be seen in the Japanese model location, but not in Germany. It was also found that an additional battery storage can lead to avoided transmission in both locations, even for large sizes of installed PV capacity.
We give the definition of s-Vector control by using the complex power vector in the p-q plane to quantify the feasible operational region of battery energy storage system (BESS) and to find the optimal power set-point solution. The s-Vector control features in the avoidance of using costly optimization solvers to solve optimization models online which is also an execution time bottleneck in real-time control. The advantages of s-Vector control are fast and stable computational efficiency. We validate a s-Vector based real-time controller of BESS to provide frequency response and voltage support as ancillary services for the power grid. The objective is to maximize the utility of the BESS asset. We formulate the dynamic capability curve of the DC-AC converter and the security requirements of the battery cells as constrains of the control system. The initial power set-points are obtained based on the traditional droop control approach. Based on s-Vector control, a fast optimization solution algorithm is proposed to find the optimal power set-point and guarantee the security. According to the experimental validation in our 720 kVA / 560 kWh BESS on EPFL campus, we achieve 100 ms of refreshing the real-time control loop. As a benchmark, using optimization solver requires 200 ms to update the real-time control loop.
The rapid growth of electric vehicles (EVs) will include electric grid stress from EV chargers and produce a large number of diminished EV batteries. EV batteries are expected to retain about 80 % of their original capacity at the end of vehicle life. Employing these in second-use battery energy storage systems (2-BESS) as energy buffers for EV chargers further reduces the environmental impact of battery manufacturing and recycling. One of the obstacles that limits performance and cost to 2-BESS is the heterogeneity of second-use batteries. In this paper, we show that a structure for power processing within a 2-BESS with hierarchical partial power processing can be optimally designed for stochastic variation in EV demand, dynamic grid constraints, and statistical variation in battery capacity. Statistically-structured hierarchical partial power processing shows better battery energy utilization, lower derating, and higher captured value in comparison to conventional partial power processing and full power processing for similar power conversion cost.
Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. The objective of the control system is to utilize the full capability of the BESSs to provide ancillary services. We take the voltage-dependent capability curve of the DC-AC converter and the security requirements of BESSs as constraints of the control system. The initial power set-points are obtained based on the droop control approach. To guarantee the feasibility of the power set-points with respect to both the converter capability and BESS security constraints, the final power set-points calculation is formulated as a nonconvex optimization problem. A convex and computationally efficient reformulation of the original control problem is then proposed. We prove that the proposed convex optimization gives the global optimal solution to the original nonconvex problem. We improve the computational performance of this algorithm by discretizing the feasible region of the optimization model. We achieve a 100 ms update time of the controller setpoint computation in the experimental validation of the utility-scale 720 kVA / 560 kWh BESS on the EPFL campus.
The paper provides a comprehensive battery storage modelling approach, which accounts for operation- and degradation-aware characteristics, i.e., variable efficiency, internal resistance growth, and capacity fade. Based on the available experimental data from the literature, we build mixed-integer linear programming compatible lithium iron phosphate (LiFePO$_4$) battery model that can be used in problems related to various applications, i.e., power system, smart grid, and vehicular applications. Such formulation allows finding the globally optimal solution using off-the-shelf academic and commercial solvers. In the numerical study, the proposed modelling approach has been applied to realistic scenarios of peak-shaving, where the importance of considering the developed models is explicitly demonstrated. For instance, a time-varying operation strategy is required to obtain the optimal utilization of the LiFePO$_4$ battery storage. Particularly, during the battery operational lifetime its optimal average SoC may change by up to $20%$, while the duration of charging process may increase by $75%$. Finally, using the same LiFePO$_4$ benchmark model from the literature, we compare the results of using the proposed approach to the state-of-the-art in the optimal sizing and scheduling problems. The proposed approach led to a $12.1%$ reduction of battery investment and operating costs compared to the state-of-the-art method.
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