Julian de Hoog

Optimal charging of electric vehicles taking distribution network constraints into account

The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer.

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.

A Market Mechanism for Electric Vehicle Charging Under Network Constraints

The increasing impact of electric vehicles on distribution networks can be alleviated by smart charging-the shifting of electric vehicle load to times when there is available capacity in the network. This work presents a market mechanism for smart charging that optimally allocates available charging capacity in a way that ensures network stability, while at the same time allowing vehicles to express individual preferences regarding their charging rates. Those who want higher rates can receive these, but must pay a higher price. The mechanism takes into account network-specific constraints such as total network load, voltage drop, and phase unbalance. However, since vehicles have differing impacts on these constraints, this leads to unequal access to the available resources (i.e., charging capacity), resulting in an unfair market. An additional constraint can be introduced to level the playing field for all users, but it leads to a reduction in aggregate performance. The mechanism is shown to be efficient and strategy-proof, so users cannot gain an unfair advantage by misrepresenting their preferences. A series of simulations demonstrate the mechanisms behavior and properties. The results open the door to multi-tiered user plans by demand response aggregators.

Dynamic team hierarchies in communication-limited multi-robot exploration

In the near future, groups of autonomous robots using wireless communication will be used for a wide variety of tasks. In many such applications, communication may be unreliable and communication ranges difficult to predict. While most current approaches to this problem strive to keep team members within range of one another, we propose an approach in which navigation and exploration beyond range limits is explicitly planned for. Robots may either explore or relay known information, and the team hierarchy corresponds to a tree. As the exploration effort unfolds, robots swap roles within this tree to improve the efficiency of exploration. Since robots reactively adjust to communication availability, the resulting behaviour is robust to limited communication. This makes it particularly suitable for applications such as robotic search and rescue, where environments are likely to contain significant interference and unexpected communication ranges.

A distributed electric vehicle charging management algorithm using only local measurements

With the uptake of electric vehicles (EVs) promoted by many governments, the impact of electric vehicles on electricity grids will become significant in the near future. In Australia, charging a typical EV battery puts the same demand per day on the grid as an average household, which could lead to a sizeable increase in peak demand. However, the negative impacts of EVs can be mitigated if their charging is scheduled during times of otherwise low demand, such as overnight. The majority of studies trying to achieve this require a certain level of coordination among EVs and/or a central controller. In many countries, however, the hardware and infrastructure required for central charging methods do not exist. Here EV charging is approached from a distributed point of view, and a protocol in which charging decisions are made individually at each household, without any access to full network state is proposed. The decision making process is conducted in real time, using both instantaneous and historical local voltage measurements to estimate present network load. The overall goal is to maximally use grid capacity at all times, while still ensuring fairness of charging for all users. The proposed algorithm ensures both charging efficiency and fairness among all EVs across the network. At the same time, peak demand in the grid is minimally affected. Simulations based on a realistic suburban network using real demand data and vehicle travel profiles is presented to illustrate typical performance.

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.

Using rendezvous to overcome communication limitations in multirobot exploration

In cooperative multirobot systems, communication can speed up completion, reduce redundancy, and prevent interference between robots. Typically, wireless point-to-point communication is used to coordinate robots. However, environmental interference, unpredictable network conditions, and distances between robots can affect the reliability of wireless communication. Therefore, approaches other than continuous message passing throughout exploration are useful. We consider the problem of coordinating a multirobot system to explore an unknown, large, open environment. An approach that uses sector search with rendezvous is presented. Robots explore an environment in sectors, or designated areas, and periodically meet to communicate map information of what they have explored. Our approach is compared to other communication paradigms in simulation. Results suggest that sector search with rendezvous is more efficient than having no communications. It further demonstrates advantages over scenarios in which robots communicate only with other robots in close proximity, and is comparable to a role-based approach with dynamic team hierarchies.

Demonstrating demand management: How intelligent EV charging can benefit everyone

This paper establishes the case for Electric Vehicle (EV) charging demand management through in-field demonstration, electricity network modelling and financial assessment. As part of the Victorian Government Electric Vehicle Trial, DiUS Computing demonstrated EV charging demand management using United Energys Smart Grid. Modelling of the United Energy network by the University of Melbourne found that uncontrolled charging would require network augmentation once EVs are adopted by 10% of households. In contrast, managed charging would allow the network to support in excess of 50% uptake using existing capacity and infrastructure. Furthermore, the end-to-end EV charging demand management solution demonstrated by DiUS could be implemented for one tenth the cost of the network augmentation. Although success factors were identified during the demonstration that may serve as an input for demand management program design, electricity market arrangements may be the strongest determinant of adoption generally.

Power Sharing in Angle Droop Controlled Microgrids

Component mismatches and parameters drifts drastically affect stability and long-term operation of droop-controlled inverter-based microgrids. This paper analyzes and illustrates the impact of design variations and parameter drifts between angle droop controlled inverter-interfaced sources in a microgrid. It is shown that microgrid stability is very sensitive to parameter drifts, especially in frequency. A coordination control scheme that uses internode communications is proposed for improving the stability margin and ensuring the desired power sharing. Conditions for stability are derived and simulation results are presented to validate the performance of the proposal.