Dynamic tariffs are expected to become a relevant pricing scheme in the context of smart grids. In this framework, active management of residential loads can play an important role to optimize the usage of end-use energy resources while minimizing energy cost. This paper presents an evolutionary algorithm to optimize the integrated usage of multiple residential energy resources (local generation, shiftable loads, thermostatically controlled loads, and storage systems) considering a large set of management strategies. Customized solution encoding and operators are developed for different groups of loads. The multiobjective model considers as objective functions the minimization of the energy cost and the minimization of end-user's dissatisfaction associated with management strategies. Results have shown that significant savings can be achieved mainly through demand response actions implemented over thermostatically controlled loads. Savings are also dependent on the end-user's preferences and degree of willingness to accept automated control.

This paper demonstrates a data-driven control approach for demand response in real-life residential buildings. The objective is to optimally schedule the heating cycles of the Domestic Hot Water (DHW) buffer to maximize the self-consumption of the local photovoltaic (PV) production. A model-based reinforcement learning technique is used to tackle the underlying sequential decision-making problem. The proposed algorithm learns the stochastic occupant behavior, predicts the PV production and takes into account the dynamics of the system. A real-life experiment with six residential buildings is performed using this algorithm. The results show that the self-consumption of the PV production is significantly increased, compared to the default thermostat control.

Given the constraints associated with grid expansion costs, limited access to reliable electricity, and priorities in addressing the climate agenda and Sustainable Development Goals in low-income countries, microgrids and off-grid solar projects represent a viable solution for rural electrification. This type of solution has the advantage of being less expensive than conventional technologies, is rapidly scalable, affordable, environmentally sustainable, and can play a critical role in empowering rural communities. In this context, this study proposed a spatial framework for off-grid solar energy planning based on a Geographical Information System and Boolean logic, Fuzzy logic, and Analytic Hierarchy Process Multicriteria Decision-Making methods. The results of the applied methodology show that the selection of optimal locations for off-grid solar photovoltaic microgrid projects in Mozambique is significantly influenced by the following order of criteria: climatology, orography, technical and location, social, and institutional criteria. Geographically, about 49% or 344,664.36 km² of the total study area is initially suitable for an off-grid solar photovoltaic microgrid project; 4% is low suitable, 14% is moderately suitable, 18% is suitable, and 13% is highly suitable. However, 51% of the ranked areas fall into the not feasible and restricted areas, mainly in conservation areas, protected areas, and areas at high risk of flooding and cyclones, covering a total of 387,005.5 km² within the study area. In general, the approach helps to reduce uncertainty and increase flexibility to identify appropriate sites and strengthen indicators of sustainable development impacts of decentralized rural electrification.