Leonel Magalhães Carvalho

Abstract

Abstract

Abstract
Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixed-integer linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.

Abstract
This paper proposes a new probabilistic model for active low-voltage prosumers suitable for distribution expansion planning studies. The load uncertainty of these consumers is considered through a range of load profiles by segmenting the energy consumption according to the different energy uses. Then, consumption adjustments are simulated using a nonhomogenous Poisson process based on the energy usage preferences and the financial gains according to the tariff scheme. A case study based on the modified IEEE 33-Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of the load profiles in scenarios with high penetration of renewable distributed generation (DG). The experiments carried out reveal that considerable monetary savings in the expansion of the distribution grid can be achieved for this case study (up to 37%) as compared with the alternative with no active demand (AD) by exploiting the flexibility associated with the active behavior of prosumers as a response to price signals and/or by permitting adequate levels for the integration of DG into the distribution grid. © 2019 John Wiley & Sons, Ltd.

Abstract
This paper proposes a 'grey-box' dynamic equivalent model for medium voltage active distribution networks, taking into account a heterogeneous fleet of generation technologies alongside the latest European grid codes requirements. It aims to properly represent the transient behavior of the system upon large voltage disturbances in the transmission side. The proposed equivalent model is composed by four main components: two equivalent generation units, one for converter-connected units' representation, and another accounting for the synchronous generation units' portfolio; an equivalent composite load model; and a battery energy storage system, also converter-connected to the grid. The model's parameters are estimated by an evolutionary particle swarm optimization algorithm, by comparing a fully-detailed model of a medium voltage distribution network with the equivalent model's frequency domain's responses of active and reactive power flows, at the boundary of distribution-transmission interface substation. © 2019 IEEE.