This study proposes a load management strategy for parking and charging facilities with the capacity to serve several hundreds of electric vehicles. The strategy is built upon two assumptions on power distribution systems of large charging stations: i) they are configured as clusters, each comprising a number of charging units for reduced cabling complexity, ii) the power delivery components (such as feeders and circuit breakers) of individual clusters are sized for load factors smaller than 100% to reduce the capital costs. Unless controlled, the load demand can concentrate into particular cluster(s) whereas other clusters can still tolerate additional demand. This may lead to avoidable load interruptions and, thus, reduced energy provision. To address this issue, a load management strategy that optimises the distribution of vehicles across the clusters and their charging profiles is proposed. The strategy is compared in simulation with a benchmark strategy in different commercial parking lot scenarios. The results demonstrate that the optimal management achieves identical demand fulfilment rates despite more pronounced load factor limitations as compared to the benchmark strategy. This can enable further reduction in system component sizing. In the tested scenarios, the proposed strategy leads to increased long term profits ranging between 12% and 43%.

Short-term control of energy storage systems (ESS) aims to find the optimal control action for the next time step in a demand management system. Several optimization models and solution strategies are presented in literature for accomplishing this task. However, there is no framework available, which enables prototyping and flexible definition of optimization problems according to changing conditions and constellation of components in real time applications and that is deployable in different embedded systems. The present work analyses the requirements imposed by the EU project Storage4Grid (S4G) and uses them as a basis for the design of an optimization framework to combine data from various sources and offer a flexible optimization-setting environment. The architecture includes modules for management and signal processing of sensor data, linking of predictive algorithms to deliver inputs to the optimization model, optimization modeling, linking of a solver, an optimization controller and a post-processer module for formatting the results or creating events. The framework is tested on three scenarios of a deterministic optimization problem and its output interface was linked to an open source power flow simulator OpenDSS to validate the results.

House and building energy management systems (HEMS) are becoming key when it comes to assure grid stability and to offer flexibility. At the same time, energy systems technology has evolved to enable energy storage systems and electric vehicles to be managed together with local generated energy taking into consideration the preferences of the household owner. Contributing to this tendency, this work presents a stochastic optimization platform (SOFW) for optimal control using dynamic programming and stochastic optimization models. A stochastic optimization model involving a household composed of photovoltaics, energy storage system and an electric vehicle is designed and tested within SOFW. The uncertainties of the plugin time and state of charge of the battery of the electric vehicle are modeled using a Markovian process and a Monte-Carlo simulation. The results showed that the proposed stochastic optimization model can be solved using dynamic programming and deployed as a continuous optimal control within SOFW. The system will be deployed shortly in Italy within one use case of the Storage4Grid (S4G) project.

An increasing penetration of EVs and their charging impose challenges to the energy grid stability. As a consequence, an optimal management of EV charging in parking lots becomes essential. This work presents an approach of a cooperative control of charging stations based on a stochastic optimization model for the energy management of a group of charging stations. Uncertainties regarding the number of charging EVs at each time step are modelled using a Markovian process, while the probability mass function was generated using a Monte Carlo simulation. Furthermore, the concept prioritizes the exploitation of local renewable resources and energy storage for EV charging to the import of electrical energy from the grid. The stochastic optimization model was integrated into our own developed Stochastic Optimization Software Framework (SOFW), which deploys the application as Model Predictive Control (MPC) in the real-time scenario using dynamic programming. The cooperative control of charging stations presented in this work was evaluated succesfully with a variety of EV driving scenarios. The approach will be validated on the field in a car park of a DSO company including renewable generation and energy storage system.

Utilization of the modular multilevel converter (MMC) topology can enable transformer-less interfacing between electric vehicle (EV) charging infrastructure and the power distribution grid. Such configurations are claimed to significantly reduce the system costs, space requirements and complexity for high-power charging facilities. On the other hand, ensuring the correct operation of such system is challenging under unevenly distributed loads in the MMC arms. Proper selection of the charging points to allocate the EVs can limit the loading unbalances between the arms and phases of the MMC system. This paper presents two load allocation strategies. The first strategy takes only the present loading into account, while the second one optimizes the decision according to the individual energy demands of the EVs over time. Simulations demonstrated that the optimized strategy minimizes loading unbalances between the MMC phases and arms during the charging operations. Furthermore, it can contribute to larger demand fulfillment.

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This paper proposes a strategy to manage an electric vehicle charging station (EVCSs) with a grid-side interface based on a Modular Multilevel Converter (MMC). The MMC topology is studied due to its potential for reducing the footprint and the use of active material in the internal distribution system by allowing for transformer-less connection to the medium voltage distribution grid. However, heterogeneous charging demands and arrival-departure profiles of the electric vehicles (EVs) could lead to significant loading unbalances among the MMC arms and among the modules of a single arm. Nevertheless, the current in the grid interface must be kept balanced and sinusoidal. Furthermore, the voltages of the modules of an arm must be balanced. This work combines a load management (LM) algorithm with a power flow management (PFM) algorithm to achieve the required characteristics of grid current and module voltages under the heterogeneity of the charging demand in MMC-based EVCSs. The PFM algorithm controls the circulating currents to compensate the phase-to-phase, arm-to-arm and intra-arm unbalances of the given loading. To minimize the additional losses resulting from active balancing by the PFM, the LM optimizes the charging schedules and allocations of incoming EVs into charging units in order to minimize phase-tophase and arm-to-arm unbalances in the system. The performance of the proposed optimization-based LM is compared with a rule-based benchmark LM by simulating the daily operation of an example shopping mall parking with MMC-based grid interface. In scenarios with pronounced unbalance limitations, the optimization-based LM increases the supplied energy significantly. Real-time (RT) simulations demonstrate a balanced and sinusoidal grid current profile and balanced module voltages in MMC arms over the daily scenarios. These results indicate that the proposed strategy combining LM and PFM is applicable for real-world deployments.

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