To Join or Not to Join? The Energy Community Dilemma: An Italian Case Study

Energy Communities (EC) are becoming a major driver to foster the energy transition in Europe and the regulatory framework adopted by each Member State (MS) plays a key role for a prosperous deployment of ECs. This paper is thus divided into two layers. The first layer of this paper addresses the current regulations introduced by MSs regarding ECs, providing a critical comparison of each solution used. The second layer of research concerns the introduction of a Mixed Integer Linear Programming (MILP) optimization algorithm early studied by some of the authors furtherly developed to assess the conditions that favour prosumers’ participation to ECs. Both these models have been tested on a case study located in the city of Magliano Alpi, in the north of Italy. The results demonstrate that the proposed methodology correctly evaluates the key parameters influencing participation of citizens in ECs and indicate that for the Italian EC under study, there is the possibility to further expand the capacity installed without undermining the profitability of investment

Short-term load forecasting in a hybrid microgrid: a case study in Tanzania

This research article published by the Journal of Electrical Systems; Vol. 15, Iss. 4, (2019)Most emerging countries such as Tanzania are promoting rural electrification through installation of microgrids. This paper proposes an approach for short-term day-ahead load forecast in rural hybrid microgrids in emerging countries. Energy4Growing research project by Politecnico di Milano department of energy in collaboration with EKOENERGY (www.ekoenergy.org) implemented in Ngarenanyuki Secondary School (Arusha, Tanzania) innovative control switchboards to form an energy smart-hub. The smart-hub was designed to manage the school’s 10kW hybrid micro-grid comprising: PV-inverter, battery storage, microhydro system, and genset. Ngarenanyuki school microgrid’s data was used for the experimental short-term load forecast in this case study. A short-term load forecast model framework consisting of hybrid feature selection and prediction model was developed using MATLAB© environment. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The objective function was feature minimization and error performance optimization. The experimental and comparative day-ahead load forecast analysis performed showed the importance of using different feature selection algorithms and formation of hybrid prediction models approach to optimize overall prediction error performance. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with ‘linearSVM’ prediction model. Furthermore, a hybrid prediction model formed from the elementwise maximum forecast instances of two regression models (‘linearSVM’ and ‘cubicSVM’) yielded better MAE prediction error than the individual regression models fused to form the hybrid

Battery modeling for microgrid design: A comparison between lithium-ion and lead acid technologies

Battery energy storage systems are fundamental components in microgrids operations, therefore it is important to adopt models suitable to properly evaluate the performance of these electrical systems. Different methodologies for battery modeling have been developed and tested in this work: (i) Empirical model, in which batteries are described by analytic expressions not based on electrochemical processes; (ii) Equivalent electrical circuit model, in which batteries are described in terms of electrical quantities. These approaches allow to adapt the model to different battery technologies: both the emerging Li-ion and the consolidated lead acid are considered in this paper. The proposed models are implemented in the software Poli. NRG, a Matlab based procedure for microgrid sizing developed by Energy Department of Politecnico di Milano. Simulations are based on a real case study relevant to a microgrid in a rural area: Ngarenanyuki Secondary School in Tanzania. The proposed methodology is used to design a new microgrid based on photovoltaic and energy storage system, comparing the results obtained adopting different modeling approaches and different technologies. Eventually, results are critically analyzed and discussed in order to compare accuracy, computational effort, costs and opportunities

Energy Communities Design Optimization in the Italian Framework

Energy communities (EC) are expected to have a pivotal role to reach European decarbonization targets. One of the key aspects is the regulatory framework adopted by each Member State to properly manage such new customers’ aggregation. The paper firstly provides an updated overview of the EC regulation, focusing on the current Italian legislation. Next, a novel methodology for the design and management of energy community initiatives is proposed. The procedure firstly solves a design and operation optimization problem to calculate the best size of energy assets (boiler, heat pump, photovoltaic, thermal storage) to be installed. Second, a Shapley value-based approach is exploited to distribute a part of the community’s incomes to members, based on their contribution to the overall welfare. Results demonstrate that the adopted methodology is effective in ensuring a proper cash flow for the community, while pushing its members towards energy efficient behaviors

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 pre-commercial sodium-nickel chloride BESS: this device is available in the IoT lab of Politecnico di Milano within the H2020 InteGRIDy project

Smart Charging Algorithm for Flexibility Provision with Electric Vehicle Fleets

The penetration of electric vehicles (EVs) is quickly increasing. On one side, they are welcome to reduce the carbon footprint of the mobility sector. On the other hand, they can have a negative impact on the power system, given the high requested power rates at low or medium voltages. To prevent this, smart charging and vehicle-to-grid algorithms must be elaborated and exploited. In this study, we propose an EV parking garage providing ancillary services on the Italian Balancing Market. The average power withdrawal is estimated based on real world data. The available power for provision of upward and downward tertiary frequency regulation are then computed. A bid on the market is performed via a Balancing Service Provider and a market simulator returns the outcome in terms of acceptance or rejection. The results include the power profiles, the reliability of the garage performance on market and the economics. The savings for energy withdrawn are around 8% of the total cost. The provided flexibility in the daily simulation is up to 5 MW for a parking with 1100 EVs. The reliability of the provision is of 99%. Improvements can include a better modeling of the market and testing different regulatory frameworks

A novel software package for the robust design of off-grid power systems

Off-grid power systems represent one of the key solutions for rural electrification. Most of the analyses or tools already present in literature do not consider the final users' energy needs as the starting point of the process nor they consider the inherent uncertainties about loads and resources. Comprehensive stochastic procedures that look at the atypical features of rural contexts and include estimation errors into the design phase are strongly required. In this paper, we present Poli. NRG (POLItecnico di Milano - Network Robust desiGn): a novel software package for the robust design of off-grid electric power systems. Poli. NRG is composed of four blocks which separately face the different design phases: (i) the data inputs gathering block provides a methodology to collect field data as regards weather condition and load demand; (ii) the inputs processing block elaborates the inputs to obtain load and sources profiles over the entire lifetime of the plant; (iii) the system modelling and simulation block simulates different off-grid system configurations and evaluates the related techno-economic performances; (iv) the output formulation block finds the most robust design for the targeted context through specific optimization methods. After a comprehensive description of the software, we have applied it to size a PV + BESS microgrid system to supply power to a peri-urban area of Uganda. The results confirm that parameters' uncertainties deeply affect the design of the system and motivate the robust design approach proposed. Poli. NRG is devoted to map those uncertainties and provide information for decision makers