Numerical and experimental efficiency estimation in household battery energy storage equipment

Battery energy storage systems (BESS) are spreading in several applications among transmission and distribution networks. Nevertheless, it is not straightforward to estimate their performances in real life working conditions. This work is aimed at identifying test power profiles for stationary residential storage applications capable of estimating BESS performance. The proposed approach is based on a clustering procedure devoted to group daily power profiles according to their battery efficiency. By performing a k-means clustering on a large dataset of load and generation profiles, four standard charge/discharge profiles have been identified to test BESS' performances. Different clustering approaches have been considered, each of them splitting the dataset according to different properties of the profiles. A well-performing clustering approach resulted, based on the adoption of reference parameters for the clustering process of the maximum power exchanged by the BESS and the variation of battery energy content. Firstly, the results have been proven through a numerical procedure based on a BESS electrical model and on the definition of a key performance index. Then, an experimental validation has been carried out on a precommercial sodium-nickel chloride BESS: this device is available in the IoT lab of Politecnico di Milano within the H2020 InteGRIDy project

Novel LoadProGen procedure for micro-grid design in emerging country scenarios: Application to energy storage sizing

This paper is devoted to describe the development, implementation and application of a novel procedure to properly design the electrification process in rural areas of Emerging Countries (EC). The procedure exploits a bottom-up approach, i.e. target applications are related to micro-grids devoted to satisfy the electrical needs of small communities. The procedure starts from microscopic data (i.e. single electric appliances) to effectively catch the customer needs (i.e. bottom) and it matches them with the available energy sources in the target area. In particular, a tool named LoadProGen developed by the Energy4Growing research group of Politecnico di Milano, is presented: the mathematical approach proposed is detailed and a real field case study relevant to a micro-grid deployed in Tanzania is provided. The tool is based on the gathering of information about the target area, i.e. to get information from interview and field audit, and on a stochastic approach to build up realistic estimation of the electric load profile of the considered uses. The energy needs forecast (cfr. load profile) is then adopted in a second procedure devoted to design a micro-grid capable to properly feed the loads. In this work, for sake of exemplification, this latter is supposed to be a photovoltaic based micro-grid integrated with an electrochemical storage

Monte Carlo Procedure to Evaluate the E-mobility Impact on the Electric Distribution Grid

The goal of this work is to evaluate the impact of the e-mobility charging processes on the electric grid, in a real-life study case. An effective approach is proposed to study the increase in the energy consumption on the grid with respect to both grid operation and efficiency. The work is developed considering three different recharging technologies, slow (based on domestic users), fast (based on public charging stations), and very fast (based on enhanced public charging stations). Furthermore, three different technologies distributions are evaluated (e.g. different scenarios on charging station deployment are simulated). The results show that fast charging technologies could better fit with e.cars exploitment, but they could cause also a significant stress increase over the grid. The paper is devoted to quantify such effects

San Severino Marche Smart Grid Pilot within the InteGRIDy project

This paper reports the presentation of a Smart Grid architecture designed with respect to the actual Italian scenario and ongoing to be experimentally validated in the distribution grid of San Severino Marche Pilot, Italy. The main characteristics of the smart grid architecture deployed are discussed and two of its innovative functionalities are detailed: reconfiguration of the Medium Voltage (MV) grid and Energy Storage Systems (ESSs) exploitation for the ancillary services provision. Such functionalities are introduced from both a theoretical and an experimental point of view. Data collected from the field have been processed by means of mathematical models in order to achieve a quantitative evaluation of the performances. Results are related to the energy losses improvement the DSO could obtain thanks to a MV grid reconfiguration and to the economic viability of ancillary services provision by means of distributed ESSs. For the first functionality, an Exhaustive Research, a Genetic Algorithm and a Monte Carlo heuristic procedure have been coded and compared. For the latter functionality, an electric model of an ESS has been exploited in order to simulate a contribution to the primary frequency control and an ancillary market model has been developed in order to evaluate a multi service management of the storage; in particular frequency response and self-consumption logics have been evaluated

Machine learning and GIS approach for electrical load assessment to increase distribution networks resilience

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach

PV forecast for the optimal operation of the medium voltage distribution network: A real-life implementation on a large scale pilot

The goal of the paper is to develop an online forecasting procedure to be adopted within the H2020 InteGRIDy project, where the main objective is to use the photovoltaic (PV) forecast for optimizing the configuration of a distribution network (DN). Real-time measurements are obtained and saved for nine photovoltaic plants in a database, together with numerical weather predictions supplied from a commercial weather forecasting service. Adopting several error metrics as a performance index, as well as a historical data set for one of the plants on the DN, a preliminary analysis is performed investigating multiple statistical methods, with the objective of finding the most suitable one in terms of accuracy and computational effort. Hourly forecasts are performed each 6 h, for a horizon of 72 h. Having found the random forest method as the most suitable one, further hyper-parameter tuning of the algorithm was performed to improve performance. Optimal results with respect to normalized root mean square error (NRMSE) were found when training the algorithm using solar irradiation and a time vector, with a dataset consisting of 21 days. It was concluded that adding more features does not improve the accuracy when adopting relatively small training sets. Furthermore, the error was not significantly affected by the horizon of the forecast, where the 72-h horizon forecast showed an error increment of slightly above 2% when compared to the 6-h forecast. Thanks to the InteGRIDy project, the proposed algorithms were tested in a large scale real-life pilot, allowing the validation of the mathematical approach, but taking also into account both, problems related to faults in the telecommunication grids, as well as errors in the data exchange and storage procedures. Such an approach is capable of providing a proper quantification of the performances in a real-life scenario

Regional energy planning based on distribution grid hosting capacity

In a liberalized energy market, policymakers cannot over-impose the deployment of new distributed generators, either in terms of location or in terms of size/technology; on the opposite, they are asked to promote incentives, penalties or constraints in order to foster a generation portfolio evolution fitting with the energy need of the loads. In the paper, given a local distribution grid, a two-step procedure is proposed to define the most effective energy policy, willing to drive a proper evolution of the generation portfolio, i.e., to maximize the renewable sources exploitation taking into account the grid constraints. The approach proposed is based on a stochastic (Monte Carlo) procedure. Given a generation portfolio, many scenarios are evaluated, changing generators' nominal power, point of common coupling and also a slightly different technologies share. Actually, the final goal of the procedure proposed is to simulate the stochastic behavior of users with respect to the regional energy policy (i.e., to perform a multidimensional sensitivity analysis) in order to validate the proposed generation portfolio. In particular, in the first step of the procedure, it is defined a portfolio in which generators are aggregated with respect to the power plant technology (PV, wind, small hydro, big hydro, etc.). Such a portfolio is optimized in order to maximize the matching between local production and local consumption. In the second step, a Monte Carlo simulation is implemented to stochastically take into account a significant number of possible configurations of each portfolio (number of generators, unit size, location, etc.). Given the generator's distribution, a probability index based on a Hosting Capacity concept is proposed as a performance index. Conductors' thermal limits and slow voltage variations on the electrical network are evaluated for several generator's distributions and for different dispersed generation penetrations. The final goal of the approach proposed is to define the optimal local generation portfolio fitting both with the load profiles and with the bounds of the distribution grid already in place. Such an output resulted to be a valuable piece of information for decisionmakers in order to properly promote regional energy planning policies. In order to validate the approach and demonstrate its capabilities, the procedure proposed has been applied to the real medium voltage distribution grid relevant to the Italian city of Aosta, i.e., real-life topologies, renewable-based generation and load fluctuation have been simulated

Energy Sharing in Renewable Energy Communities: The Italian Case

Renewable energy communities (RECs) are recently established legal entities that allow European citizens, local authorities and SMEs to cooperate in the generation, supply and sharing of electrical energy from renewable energy sources. The competitiveness of RECs with respect to traditional market operators is not straightforward, and the investment in a new REC has to be carefully assessed. This paper presents a methodology that could support the decision process of citizens investing in a new REC. The main goal of the methodology is the optimization of the production portfolio of the community, considering the energy sources in the availability of community members, their different energy needs and tariffs. A model of community capable to evaluate energy flows among members is presented and it is adopted for the optimal investment evaluation. The optimal solution is the portfolio that maximises the net present value of the investment and it is obtained via a genetic algorithm. Applying the methodology to a case study, the economical feasibility of a REC in the Italian framework is investigated, considering four different energy strategies and two incentive policies

Microgrid design and operation for sensible loads: Lacor hospital case study in Uganda

The paper provides a methodology for the techno-economic optimization of microgrid systems and its application on the case study of St. Mary Lacor hospital of Gulu, Uganda. The low reliability of the Ugandan national grid represents a barrier for the operation of the infrastructures in the hospital and leads to extra costs for back-up solutions. Authors performed a two months data collection campaign, then the electrical load of the hospital has been simulated adopting two hundred realistic profiles obtained by means of a Monte Carlo procedure based on an on-site survey. Such data has been adopted in order to design a theoretical new microgrid capable to optimally feed the loads operating both in a grid tied and in a stand-alone configuration. One of the main goals of the microgrid is in the maximization of the energy supply reliability. The optimization method developed is based on the Poli.NRG tool, developed by Politecnico di Milano; it defines the optimal size of the system components as well as their dispatch strategy. The numerical results of the simulation could be adopted by the Hospital managers in order to evaluate new generators to be deployed in the facility and to identify the best energy policy