Local Energy Communities (LECs) can facilitate the transition towards sustainable and clean energy system infrastructure. In this work, we construct a novel hierarchical energy management framework for an LEC equipped with a community energy storage (CES) installation. The proposed two-stage approach involves end-users making self-driven, cost-optimal decisions (first stage) and said decisions being further coordinated through the CES in service of boosting the LEC’s self-consumption and self-sufficiency (second stage). By complementing the approach with a real-time, predictive, envelope-based methodology for LEC flexibility quantification, a rigorous cost structure for flexibility procurement is established. The LEC is further considered to be providing higher-level ancillary services to the wider power grid, through the load flexibility provision and CES capacity sharing. The efficacy and superior performance of the proposed approach are demonstrated in an exhaustive case study. This includes a detailed comparison with conventional centralized approaches and a comprehensive analysis of the financial and environmental benefits that this envisioned LEC variant can ultimately achieve.

The EU Directive 2018/2001 recognized wastewater as a renewable heat source. Wastewater from domestic, industrial and commercial developments maintains considerable amounts of thermal energy after discharging into the sewer system. It is possible to recover this heat by using technologies like heat exchangers and heat pumps; and to reuse it to satisfy heating demands. This paper presents a review of the literature on wastewater heat recovery (WWHR) and its potential at different scales within the sewer system, including the component level, building level, sewer pipe network level, and wastewater treatment plant (WWTP) level. A systematic review is provided of the benefits and challenges of WWHR across each of these levels taking into consideration technical, economic and environmental aspects. This study analyzes important attributes of WWHR such as temperature and flow dynamics of the sewer system, impacts of WWHR on the environment, and legal regulations involved. Existing gaps in the WWHR field are also identified. It is concluded that WWHR has a significant potential to supply clean energy at a scale ranging from buildings to large communities and districts. Further attention to WWHR is needed from the research community, policymakers and other stakeholders to realize the full potential of this valuable renewable heat source.

In this work, a new robust controller is proposed for building climate control in presence of parametric uncertainties. The design of the controller is based on the Model Predictive Control (MPC) framework and it includes time-varying constraints. The robust design is implemented by explicitly considering parametric uncertainty in the synthesis of the control law. Variations of the parameters of the buildings are represented in the form of polytopic uncertainty. The robust control action is obtained by minimizing an appropriate ‘worst-case’ cost function, which leads to the definition of a min–max optimization problem. This optimization problem is formulated using Linear Matrix Inequalities (LMIs) that allow for efficient numerical computation of the control command. Simulation results show that the proposed approach is successful in keeping the indoor temperature of the building in the desired range even in presence of large model uncertainties. The proposed controller is also compared with a nominal controller synthesized without accounting for parametric uncertainty. Numerical results confirm 24% better performance of the robust design in comparison with the nominal controller with same conditions. Further, simulation results also demonstrate that the robust control system achieves 17% better performance in the case of severe conditions of uncertainty.

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption.