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Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads toward production periods. Therefore, this article introduces a systematic approach to provide a preliminary evaluation of the thermoeconomic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an ϵ-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed-integer linear programming technique. To illustrate the contribution of MPC, different applications on the single-and multi-dwelling level are presented and analyzed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single-building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.

This paper presents a flexible and modular control scheme based on distributed model predictive control (DMPC) to achieve optimal operation of decentralized energy systems in smart grids. The proposed approach is used to coordinate multiple distributed energy resources (DERs) in a low voltage (LV) microgrid and therefore, allow virtual power plant (VPP) operation. A sequential and iterative DMPC formulation is shown which incorporates global grid targets along with the local comfort requirements and performance indices. The preliminary results generated by the simulation of a studied case proves the benefits of applying such a control scheme to a benchmark low voltage microgrid

In order to fully exploit the potential of renewable energy resources (RERs) for building applications, optimal design and control of the different energy systems is a compelling challenge to address. This paper presents a two-step multi-objective optimization approach to size both thermal and electrical energy systems in regard of thermo-economic performance indicators to suit consumer and grid operator interests. Several utilities such as storage, conversion systems, and RERs are hence modelled and formulated through mixed-integer linear programming. Simultaneously, the algorithm defines the optimal operation strategy, based on a model predictive control structure, for each deterministic unit embedded within the energy management system of the building to meet the different comfort and service requirements.The developed design framework is successfully applied on several energy systems configuration of typical Swiss building types. Different component sizes are analysed, regarding the present investment cost and the self-consumption share. In addition, this paper presents a novel optimal design criteria based on the maximum cost benefits in the view of both the consumer and the distribution network operator.Keywords: Multi-Objective Optimization, Optimal design and control, Distributed Energy System, System Modelling, Complete building simulation Highlights• K-medoids clustering of the input data to improve computational efforts • Advanced thermal modelling by applying discrete control-oriented models of thermo-electrical energy systems and heat cascading. * Corresponding author, Phone Number: +41 21 695 82 60

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