This paper presents an overview of the work under development within MAESTRI EU-funded collaborative project. The MAESTRI Total Efficiency Framework (MTEF) aims to advance the sustainability of manufacturing and process industries by providing a management system in the form of a flexible and scalable platform and methodology. The MTEF is based on four pillars: a) an effective management system targeted at process continuous improvement; b) Efficiency assessment tools to support improvements, optimisation strategies and decision support; c) Industrial Symbiosis paradigm to gain value from waste and energy exchange; d) an Internet-of-Things infrastructure to support easy integration and data exchange among shop-floor, business systems and tools.
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.
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.
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