Search

• Introduction of the collaborative paradigm for energy management in micro-grids. • Formulation of a bi-Objective Mixed Integer Linear Programming model for it. • Employment of the Augmented ϵ-constraint approach to solve the problem. • Evaluation of the proposed method in comparison with the Weighted-Sum technique. • Experiments prove the beneficial effects of collaborative energy management policies. In micro-grids, distributed energy generation based on renewable sources allows reducing the fossil fuel emissions. In order to manage the limited availability of renewable sources and to meet users' requirements, a proper scheduling of both tasks and storage activities is needed. Moreover, the difficulty of storing energy on a large scale represents an opportunity for the micro-grid actors to collaborate for better balancing energy supply and demand. Collaboration may be achieved through the application of suitable energy management policies tackling the specific consumers' needs. We address the Energy Management Problem in a micro-grid with collaboration among users. Specifically, a building is considered where each apartment can use both Shared and Local energy resources, the former managed by a Building Manager (BM). The problem is formulated through Bi-objective Mixed Integer Linear Programming , maximizing both the BM and the apartments' profits and solved through the Augmented ϵ -Constraint approach. Numerical results, obtained on a set of realistic scenarios, are compared to those of the Weighted Sum method and evaluated according to proper performance metrics. The approach flexibility, in terms of selected management policies, is also discussed. Finally, the superiority of the collaborative paradigm versus the non-collaborative one is experimentally proven. [ABSTRACT FROM AUTHOR]

The paper presents the operational results of a real life residential microgrid which includes six apartments, a 20 kWp photovoltaic plant, a solar based thermal energy plant, a geothermal heat pump, a thermal energy storage, in the form of a 1300 l water tank and two 5.8 kW h batteries supplying, each, a couple of apartments. Thanks to the thermal energy storage, the solar based thermal energy plant is able to satisfy the 100% of the hot water summer demand. Therefore the thermal energy storage represents a fundamental element in the management of the residential demand of thermal energy. It collects renewable thermal energy during day-time to release it during night-time, effectively shaving the peak of the thermal energy demand. The two electric storages, on the other hand, provide the hosted electrical subsystems with the ability to effectively increase the self-consumption of the local energy production, thus lowering the amount of energy surplus to be sold back to the grid, and increasing the self-sufficiency of the microgrid. For instance, the storage has supported self-consumption up to the 58.1% of local energy production with regard to the first battery, and up to the 63.5% with regard to the second one. Also, 3165 and 3365 yearly hours of fully autonomous activity have been recorded thanks to the first, and the second battery respectively. On the other hand, the yearly average efficiency amounts to 63.7%, and 65.3% respectively, for the first and second battery. In the second part of the paper we propose a computational framework to evaluate the overall performance of the microgrid system, while accounting different operating conditions and energy management policies. From this perspective, the framework acts as a useful modeling and design tool, to assess the opportunity of employing alternative energy management system topologies and strategies. Eight different configurations, with growing complexity, have been derived from the original system on purpose. The simulations, carried out based on real data related to one-year time period, have provided results showing that, the higher the integration level of electrical and thermal storage is, the higher degree of self-sufficiency can be achieved by the microgrid, and, in turn, the more consistent the yearly energy saving become. Nevertheless, despite the energy cost reduction achievable with the availability of storage systems in the Leaf House, their high investment cost made them not really profitable at the current price conditions for devices and energy purchase. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the joint use of active and reactive power in the Additive Factorial Hidden Markov Models framework. In particular, in the proposed approach, the appliance model is represented by a bivariate Hidden Markov Model whose emitted symbols are the joint active-reactive power signals. The disaggregation is performed by means of an alternative formulation of the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm for dealing with the bivariate HMM models. The proposed solution has been compared to the original AFAMAP algorithm based on the active power only and to the seminal approach proposed by Hart (1992), based on finite state machine appliance models and which employs both the active and reactive power. Hart’s algorithm has been improved for handling the occurrence of multiple solutions by means of a Maximum A Posteriori technique (MAP). The experiments have been conducted on the AMPds dataset in noised and denoised conditions and the performance evaluated by using the F 1 -Measure and the normalized disaggregation metrics. In terms of F 1 -Measure, the results showed that the proposed approach outperforms AFAMAP, Hart’s algorithm, and Hart’s with MAP respectively by + 14.9 % , + 21.8 % , and + 2.5 % in the 6 appliances denoised case study. In the 6 appliances noised case study, the relative performance improvement is + 25.5 % , + 51.1 % , and + 6.7 % . [ABSTRACT FROM AUTHOR]