Mattia Marinelli

Optimal Management Strategy of a Battery-Based Storage System to Improve Renewable Energy Integration in Distribution Networks

The paper proposes the modeling and the optimal management of a hot-temperature (sodium nickel chloride) battery system coupled with wind generators connected to a medium voltage grid. A discrete-time model of the storage device reproducing the battery main dynamics (i.e., state of charge, temperature, current, protection, and limitation systems) has been developed. The model has been validated through some experimental tests. An optimal management strategy has been implemented based on a forward dynamic programming algorithm, specifically developed to exploit the energy price arbitrage along the optimization time horizon (“generation shifting”). Taking advantage of this strategy wind generation performances can be enhanced and adapted to load demand, obtaining an increased economic gain measured by the difference between the economic revenue obtained with and without the proposed generation shifting policy.

Testing of a Predictive Control Strategy for Balancing Renewable Sources in a Microgrid

This paper presents the design of a control strategy for the energy management of a grid-connected microgrid with local distributed energy resources as: 10-kW photovoltaic plant, 11-kW wind turbine, and 15-kW-190-kWh vanadium-based electric storage system. According to future regulations, the renewable energy producers will also have to provide a day-ahead hourly production plan. The overall idea is, by knowing the meteorological forecasts for the next 24 h, to dispatch the microgrid in order to be able to grant the scheduled hourly production by means of proper management of the storage system. The usage of the storage system is, however, minimized by the energy management strategy. The system design is validated by experimental testing carried out in SYSLAB, a distributed power system test facility at Riso Campus, Technical University of Denmark.

Scheduling of domestic water heater power demand for maximizing PV self-consumption using model predictive control

This paper presents a model predictive control (MPC) strategy for maximizing photo-voltaic (PV) self-consumption in a household context exploiting the flexible demand of an electric water heater. The predictive controller uses a water heater model and forecast of the hot water consumption in order to predict the future temperature of the water and it manages its state (on and off) according to the forecasted PV production, which are computed starting from forecast of the solar irradiance. Simulations for the proof of concept and for validating the proposed control strategy are proposed. Results of the control approach are compared with a traditional thermostatic controller using historical measurements of a 10 kW PV installation. Economic results based on the Italian self consumption tariffs are also reported. The model of the water heater complex is a mixed grey and white box and its parameters have been estimated using a real water heater device.

Analysis of voltage support by electric vehicles and photovoltaic in a real Danish low voltage network

With conventional generating units being replaced by renewable sources which are not required to provide same high level of ancillary services, there is an increasing need for additional resources to achieve certain standards regarding frequency and voltage. This paper investigates the potential of incorporating electric vehicles (EVs) in a low voltage distribution network with high penetration of photovoltaic installations (PVs), and focuses on analysing potential voltage support functions from EVs and PVs. In addition, the paper evaluates the benefits that reactive power control may provide with addressing the issues regarding voltage control at the expense of increased loading. Analysed real Danish low voltage network has been modelled in Matlab SimPowerSystems and is based on consumption and PV production data measured individually for number of households.

Enhancing the Role of Electric Vehicles in the Power Grid: Field Validation of Multiple Ancillary Services

With increased penetration of distributed energy resources and electric vehicles (EVs), different EV integration strategies can be used for mitigating various adverse effects, and supporting the grid. However, the research regarding EV smart charging has mostly remained on simulations, whereas the experimental validation has rarely been touched upon. This paper focuses mainly on evaluating the technical feasibility of a series-produced EV to provide flexibility in real distribution grids. The implemented controller uses contemporary and widely supported standards for limiting the EV charging rate, which essentially means that it is applicable to any EV complying with IEC 61851 and SAE J1772 standards. The field test validation is conducted in a real Danish distribution grid with a Nissan Leaf providing three ancillary services through unidirectional ac charging, namely, congestion management, local voltage support, and primary frequency regulation. Several performance parameters, such as EV response time and accuracy, are assessed and benchmarked with current requirements. Ultimately, this paper aims to strengthen the applied research within the EV integration domain through validating smart grid concepts on original standard-compliant equipment.

Wind and Photovoltaic Large-Scale Regional Models for Hourly Production Evaluation

This work presents two large-scale regional models used for the evaluation of normalized power output from wind turbines and photovoltaic power plants on a European regional scale. The models give an estimate of renewable production on a regional scale with 1h resolution, starting from a mesoscale meteorological data input and taking in account the characteristics of different plants technologies and spatial distribution. An evaluation of the hourly forecasted energy production on a regional scale would be very valuable for the transmission system operators when making the long-term planning of the transmission system, especially regarding the cross-border power flows. The tuning of these regional models is done using historical meteorological data acquired on a per-country basis and using publicly available data of installed capacity.

Grey-box modeling for system identification of household refrigerators: A step toward smart appliances

This paper presents the grey-box modeling of a vapor-compression refrigeration system for residential applications based on maximum likelihood estimation of parameters in stochastic differential equations. Models obtained are useful in the view of controlling refrigerators as flexible consumption units, which operation can be shifted within temperature and operational constraints. Even if the refrigerators are not intended to be used as smart loads, validated models are useful in predicting units consumption. This information can increase the optimality of the management of other flexible units, such as heat pumps for space heating, in order to smooth the load factor during peak hours, enhance reliability and efficiency in power networks and reduce operational costs.

Distribution grid services and flexibility provision by electric vehicles: A review of options

Due to the increasing penetration of distributed generation and new high-power consumption loads - such as electric vehicles (EVs) - distribution system operators (DSO) are facing new grid security challenges. DSOs have historically dealt with such issues by making investments in grid reinforcement. However, an alternative solution, enabled by the expected roll-out of smart meters and high penetration of flexible loads, would be the increased use of flexibility services. Flexible loads, with EVs at their forefront, can modulate their consumption or even inject power back to the grid depending on current grid conditions. In return, flexibility provision should be remunerated accordingly. In this paper, the authors are interested in making an accurate description of the flexibility services at the distribution level which could be provided by EVs as well as their requirements, e.g. location, activation time and duration. Market design recommendations for enhancing the provision of DSO grid services by EVs are derived from the conducted analysis.

Voltage management in unbalanced low voltage networks using a decoupled phase-tap-changer transformer

The paper studies a medium voltage-low voltage transformer with a decoupled on load tap changer capability on each phase. The overall objective is the evaluation of the potential benefits on a low voltage network of such possibility. A realistic Danish low voltage network is used for the analysis. The load profiles are characterized by using single phase measurement data on voltages, currents and active powers with a 10 minutes resolution. Different scenarios are considered: no tap action, three-phase coordinated tap action, single phase discrete step and single phase continuous tap action. The effectiveness of the tapping capability is evaluated by comparing the Voltage Unbalance Factor and the voltage levels on the neutral cable.

Power and energy control strategies for a Vanadium Redox Flow Battery and wind farm combined system

The paper aims at describing two different control strategies for a combined system composed by a Vanadium Redox Flow Battery and a wind farm. A brief overview of the dynamic models used at describing the storage system and the wind turbines is presented. The focus is then devoted to the description of the two controllers, which task is to grant the desired power output at the point of connection of the system to the main network. The two control strategies called respectively Power Control and Energy Control are analyzed and their effectiveness is tested. The wind turbines are, in fact, fed with turbulent winds and the storage is controlled to perform a series of charges and discharges in order to have the desired global output. Their implementation and the dynamic simulations are performed in the Matlab-Simulink environment.