Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies
Introduction
Power grids are going to face several challenges, such as the increasing diffusion of distributed generation technologies [1], [2], [3], many including renewable energy sources [4], [5], [6], [7]. Other challenges come from the integration and connection, at local scale, between electric and thermal networks (but also electric mobility in the near future) [8], [9], [10], [11], [12]. Moreover, with the adoption of demand side management (DSM) strategies, final consumers are going to play an active role in the grid activity [13], [14], [15], [16], [17], [18], [19]. In this context, energy storage technologies have a key role for several reasons. In the first place, they represent the means to match energy production from renewable sources and energy demand [20], [21]. Even more so in residential scenarios, since the load profile often describes the typical energy demand of an employee, who needs hot water mainly early in the morning and late in the afternoon, with a minimum contemporary factor with solar production. Thermal energy storage allows to collect renewable energy during day-time and to use it during night-time. In addition, energy storage devices enhance the energy self-consumption level achievable by final users. Thermal energy storages, in particular, help in reducing the burden on service utilities (natural gas or electricity). Electrical energy storages double the benefit. On the one hand, it lowers the burden on the power grid. On the other hand, it reduces, or prevents altogether, depending on circumstances, the bidirectional flux of energy, from and towards the grid. It compensates for the main obstacle against renewable energy source widespread, that is the aleatory nature of renewable energy source availability [22], [23], [24]. The third reason, which is related to thermal storage, if tailored on purpose, is the ability to increase the energy yield of solar based thermal energy plants [25]. In conclusion, energy storages improve the flexibility of DSM strategies, enhance the final user energy demand profile, and also minimize the overall energy bill [19], [26].
In the transition from nowadays power grid technology to the smart grid technology, microgrids play a fundamental role as small scale test bench of DSM strategies [27], [28], [29].
In this paper we present a residential microgrid, the Leaf House, which accounts six apartments, a photovoltaic (PV) energy production plant, a solar based thermal energy production plant, a geothermal heat pump, a thermal energy storage in the form of a water tank of 1300 l and two batteries of 5.8 kW h each. The Leaf House hosts a building automation and monitoring system which makes it an ideal test field for energy storage systems applications. By recording and collecting the data resulting from the everyday life of its lodgers, the Leaf House is a living lab that records real life energy demand profiles. Also the performance of both electrical and thermal storages are evaluated on real life operating conditions, rather than in simulated ones.
A relevant contribution of this work is the computational framework aimed at micro-grid design, which serves as a tool to model and simulate the energy management occurring within the Leaf House electrical system. It has been used to simulate the environment behavior over a one-year time horizon, accounting different storage management strategies and various system configurations. The suitability of computational tools to monitor, control and simulate the smart grid behavior in different operating conditions and at different abstraction levels, has been extensively shown and commented in literature [30], [31], [32], [33]. The proposed framework is based on the Mixed-Integer Linear Programming (MILP) paradigm, successfully used for energy management purposes by some of the authors in recent publications [34], [35], but not yet proposed as a design tool in the evaluation of case studies based on a real life environment such as the Leaf House. The MILP approach has shown its effectiveness and capability, in dealing with a large number of constraints, with respect to other computational intelligence techniques [36], [37], [38]. The framework includes, also, a Neural Network based software for solar power forecasting. The simulations carried out in this work have provided the means to evaluate the yearly energy overall cost for each of the addressed configurations. Therefore, the importance of the electrical and thermal energy storages within the system has been evaluated, but also the fact that the Leaf House energy management system can be improved with the adoption of adequate hardware modifications and storage management strategies.
The paper is organized as follows: Section 2 presents the microgrid used as reference, along with operational results and related comments. In Section 3 the energy management simulation framework and the solar power forecasting algorithm are presented, whereas Section 4 presents the case study and the scenarios being examined. The simulation results are reported and discussed in Section 5. Section 6 draws the conclusions of the work.
Section snippets
Microgrid under study: the Leaf House building
The Leaf House (see Fig. 1) is one of the six international case studies selected by the IEA Task 40/ECBCS Annex 52: “Towards Net Zero Energy Solar Buildings” [39], [40]. Built in 2008, the Leaf House is located in Angeli di Rosora, Ancona, Italy (latitude 43°28′43.16 N, longitude 13°04′03.65 E, altitude 130 m above sea level). The site is characterized by a moderate climate: annual temperature between −5 and 37 °C; 1688 degree day, mean annual horizontal solar radiation 302 W/m2. The Leaf House
Energy management simulation framework
The suggested framework is meant to simulate the behavior of the energy management subsystem at a high level of abstraction. The aim is to focus on the energy fluxes and their balance, so that the energy management subsystem can be tailored and evaluated early in time, during the design process. Moreover, as a design tool that focuses on the energy flows, the devised framework does not require to simulate the system from a physical point of view. In other words there is no need to account the
Case study and scenarios
As mentioned above, the suggested framework is intended as a design aid, meant to evaluate the performance of different energy management alternative systems, which are to be integrated in the microgrid context of choice. It follows that, given the needs and habits of the user, the designer can identify the best performing solution early in time, without the need of an in depth analysis of each candidate solution. To evaluate the performance of the framework as a design tool, a real life energy
Results and comments
In the first test, the energy management is simulated, based on historical data, for each of the proposed configurations. The performance is summarized in Table 4, which reports, in each column, the results of the corresponding configurations.
The reported amounts present, in order, the cost of the energy purchased to supply the electrical blocks and the cost of the energy purchased to supply the thermal blocks. Next, the overall energy purchased amount, and surplus are reported. At last, the
Conclusions
In the first part of the paper we presented the operational results of a residential microgrid, the Leaf House, composed by six apartments, equipped with a 20 kWp PV plant, a solar thermal plant, a geothermal heat pump, a hot water thermal storage of 1300 l and two 5.8 kW h batteries, each serving a couple of apartments. Thermal energy storage has been demonstrated to be a fundamental component for residential demand side management. Indeed, it has allowed both to smooth thermal peak-power of the
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