Grid stability becomes an issue when incorporating renewable distribution generation into an electrical grid due to voltage fluctuations. This work presents an innovative solution which assists grid planners in carrying out technical and economic analysis of future grids and in taking decisions based on it. A set of tools allows the determination of the renewable energy sources and energy storage systems impact to a given grid concerning technical and economic indicators. Using these tools, a study was conducted comparing model predictive control with photovoltaics-curtailment, volt-watt and volt-var methods for the control of photovoltaics and energy storage power in an existing grid. Some highlights of the analysis are: (i) the given grid supports maximal photovoltaics penetration level of 120% without exceeding the ±10% voltage level limits; (ii) the model predictive control method aiming at the minimization of power exchange in a grid with 60% storage penetration allowed significant increase of photovoltaics penetration to 190% and reduced the maximum voltage level to 1.089pu; (iii) a user-centered design and development of the interface for grid planners resulted in a system usability scale score of 63.9. The tools enable the grid planner to take decisions when planning the future grid.

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 plug-in 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.

Demand-side flexibility management is a key enabler of the transformation towards the high penetration of renewable energy resources. We present a flexibility-management system called Flex4Grid, which is designed to provide a low-cost solution for residential consumers wishing to participate in power-grid balancing. The Flex4Grid system continuously forecasts the need for flexibility in a power grid and informs consumers about the flexibility-management periods. Consumers can provide their flexibility to an aggregator in exchange for a reward, which depends on the selected incentive scheme. The automation of the flexibility-management events is provided by interfacing with devices and the system via the Z-Wave and open platform communication unified architecture (OPC UA) technologies. The Flex4Grid system has been deployed in three pilots in Slovenia and Germany. A large-scale pilot in Celje, Slovenia, with 1047 participants, was used to collect statistical data regarding how consumers participate in the flexibility-management events. A critical peak-pricing incentive scheme was used in the Celje pilot. The smaller German pilots with a total of 185 participants were used for testing the technical capabilities of the system. User-satisfaction surveys were performed in all three pilots. The results indicate that the proposed approach is appropriate for engaging consumers in flexibility-management events. On average, the pilots' participants reduced their load by 10% during a peak event. The overall scores of the user-satisfaction survey were 3.4 and 3.9 on a 5-point Likert scale for the German and Slovenian pilots, respectively. These are good results for a prototype system; however, improvements to the stability and usability of the system are required.

Storage4Grid (S4G) is a 36-months project funded by the European Commission under the call LCE-01-2016, area 1 (Storage). S4G proposes an innovative holistic methodology for modelling, planning, integrating, operating and evaluating distributed Energy Storage Systems (ESS) including storage at user premises and storage at substation level, Electrical Vehicles (EV), innovative energy metering and energy routing technologies. This paper presents a systematic strategy used by S4G to generate the use cases for two scenarios related to two test sites: in Bolzano focused on cooperative EV charging and in Fur addressing the impact of user and grid side storage coordination to the medium and low voltage distribution network. The paper summarizes the S4G vision, solutions, and approaches for evolving the cooperative and coordinated ESS scenarios into use-cases and related requirements.

Internet of Things (IoT) technologies are widely recognized as key components of upcoming Smart Cities; however, pure technology is not sufficient for successful instantiation of the underlying vision of future urban settlements. Cities are mainly for humans, supporting their businesses, and involving the needed administrative entities. These existing stakeholders shall find new ways of interaction and new paradigms enabled by the IoT technological infrastructure to actually enact the full smart city vision. This chapter describes the steps along the tough and challenging path toward actual Smart Cities, pursued by ALMANAC (Reliable Smart Secure Internet Of Things For Smart Cities), an FP7-project cofunded by the seventh Framework Programme of the European Union.