Valentin Muenzel

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

The expected rise of electric vehicles will lead to significant additional demand on low voltage (LV) distribution systems. Uncontrolled charging could lead to problems such as thermal overload of transformers and lines, voltage deviation, harmonics, and phase unbalance. We propose two electric vehicle charging algorithms, one centralized and one distributed, and compare their performance in simulations that use real vehicle data, on a model based on a real LV network in northern Melbourne, Australia. Our experiments confirm that the locations of the vehicles in the network are an important factor in predicting adverse effects. Furthermore, our coordinated charging solutions allow penetrations of electric vehicles approximately 3-6 times higher than is possible using uncoordinated charging, in our network.

Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation

Energy storage systems have the potential to deliver value in multiple ways, and these must be traded off against one another. An operational strategy that aims to maximize the returned value of such a system can often be significantly improved with the use of forecasting — of demand, generation, and pricing — but consideration of battery degradation is important too. This paper proposes a stochastic dynamic programming approach to optimally operate an energy storage system across a receding horizon. The method operates an energy storage asset to deliver maximal lifetime value, by using available forecasts and by applying a multi-factor battery degradation model that takes into account operational impacts on system degradation. Applying the method to a dataset of a residential Australian customer base demonstrates that an optimally operated system returns a lifetime value which is 160% more, on average, than that of the same system operated using a set-point-based method applied in many settings today.

PV generation and demand mismatch: Evaluating the potential of residential storage

Favourable conditions in recent years have led to significant uptake of residential rooftop solar photovoltaic generation in many parts of the world. However, the cost-effectiveness of such systems is reducing due to declining subsidies, falling feed-in tariffs, and the typical timing mismatch between solar generation and local demand. In this paper, we investigate how this mismatch can be addressed by installing a customer-end storage system that provides an opportunity to maximally exploit the value of existing solar generation. The value of such storage depends on the extent of the coincidence of demand and generation, the size of the storage system, the pricing structure for both energy used and energy generated, any available feed-in tariffs, and the cost of the storage itself. An optimal storage operational strategy using dynamic programming is introduced and a variety of storage system sizes and price scenarios are evaluated and compared. Our study shows that under certain conditions customer-end storage could become economically attractive to consumers in the near future, opening the door for disruptive retail electricity business models in the years to come.

A Multi-Factor Battery Cycle Life Prediction Methodology for Optimal Battery Management

Affordability of battery energy storage critically depends on low capital cost and high lifespan. Estimating battery life-span, and optimising battery management to increase it, is difficult given the associated complex, multi-factor ageing process. In this paper we present a battery life prediction methodology tailored towards operational optimisation of battery management. The methodology is able to consider a multitude of dynamically changing cycling parameters. For lithium-ion (Li-ion) cells, the methodology has been tailored to consider five operational factors: charging and discharging currents, minimum and maximum cycling limits, and operating temperature. These are captured within four independent models, which are tuned using experimental battery data. Incorporation of dynamically changing factors is done using rainflow counting and discretisation. The resulting methodology is designed for solving optimal battery operation problems. Implementation of the methodology is presented for two case studies: a smartphone battery, and a household with battery storage alongside solar generation. For a smartphone that charges daily, our analysis finds that the battery life can be more than doubled if the maximum charging limit is chosen strategically. And for the battery supporting domestic solar, it is found that the impact of large daily cycling outweighs that of small more frequent cycles. This suggests that stationary Li-ion batteries may be well suited to provide ancillary services as a secondary function. The developed methodology and demonstrated use cases represent a key step towards maximising the cost-benefit of Li-ion batteries for any given application.

On making energy demand and network constraints compatible in the last mile of the power grid

Abstract In the classical electricity grid power demand is nearly instantaneously matched by power supply. In this paradigm, the changes in power demand in a low voltage distribution grid are essentially nothing but a disturbance that is compensated for by control at the generators. The disadvantage of this methodology is that it necessarily leads to a transmission and distribution network that must cater for peak demand. So-called smart meters and smart grid technologies provide an opportunity to change this paradigm by using demand side energy storage to moderate instantaneous power demand so as to facilitate the supply-demand match within network limitations. A receding horizon model predictive control method can be used to implement this idea. In this paradigm demand is matched with supply, such that the required customer energy needs are met but power demand is moderated, while ensuring that power flow in the grid is maintained within the safe operating region, and in particular peak demand is limited. This enables a much higher utilisation of the available grid infrastructure, as it reduces the peak-to-base demand ratio as compared to the classical control methodology of power supply following power demand. This paper investigates this approach for matching energy demand to generation in the last mile of the power grid while maintaining all network constraints through a number of case studies involving the charging of electric vehicles in a typical suburban low voltage distribution network in Melbourne, Australia.

Battery Management using Secondary Loads: A Novel Integrated Approach

Abstract Large lithium-ion battery systems for electric vehicle and stationary storage applications use from tens to hundreds of series-connected cells. In this paper we present an advanced integrated management structure and method that uses intelligent switching to balance series-connected cells. Our analysis shows that using this approach can increase the useful system capacity by upwards of 7% compared to conventional active or passive balancing systems by fully utilising cells with different capacities. The battery system efficiency in an electric vehicle has also been shown to increase by up to 3% compared to conventional systems. Further advantages of integrated balancing include simultaneous integration of both primary and secondary power loads or sources, as well as robustness to individual cell failure. This indicates significant potential for implementation in future lithium-ion battery packs.