• Energy Research

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

Factors such as uncertainty associated to fuel prices, energy demand and generation availability, are on the basis of the agents major concerns in electricity markets. Facing that reality, price forecasting has an increasing impact in agents' activity. The success on bidding strategies or on price negotiation for bilateral contracts is directly dependent on the accuracy of the price forecast. However, taking decisions based only on a single forecasted value is not a good practice in risk management. The work presented in this paper makes use of artificial neural networks to find the market price for a given period, with a certain confidence level. Historical information was used to train the neural networks and the number of neural networks used is dependent of the number of clusters found on that data. K-Means clustering method is used to find clusters. A study case with real data is presented and discussed in detail

The unplanned power outages have occurred for all of us, and we know that from the household inhabitant’s standpoint, power outage even for a short period of time is not pleasant. This matter is also true for the residential buildings that are far from the main cities, namely in the countryside or in a small village, and they may have a power outage for a long-term even with a small problem in the power transmission lines. Therefore, an energy solution module is required in order to prevent power outage for household inhabitants. In this paper, an off-grid energy sustainability solution for residential buildings will be represented by considering several small-scale generators. The module consists of several microgeneration units and energy storage systems for maintaining the energy balance. Also, a software-based control unit is used for controlling the production. The main focus is given to survey the performance of the wind turbine utilized in this energy module. The behavior of an induction machine and a permanent magnet machine will be investigated in two levels of simulation and emulation in order to realize the best solution for the wind turbine of presented energy module. UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.

In order to overcome the consequences of high energy consumption, renewable energy resources advancement, smart grids development, and demand response programs effectiveness should be noticed and surveyed. Regarding the important role of buildings in energy consumption, another important factor is equipping the buildings and houses with automation. For this purpose, this paper presents an optimization algorithm based on demand response program which is concerning about time of use for ancillary service and also is based on energy price in different periods. In the case study of this paper, the power consumption of two washing machines and one dryer are considered to achieve the demand response program goal. Since it is not reasonable to use dryer before washing machines, the sequence of operation cycle of devices is important for the algorithm. The obtained results of the algorithm show the applied load shifting based on energy price, demand response requirements and remuneration.

[EN] This paper addresses the theme automated bilateral negotiation of energy contracts. In this work, the automatic combination between different negotiation tactics is proposed. This combination is done dynamically throughout the negotiation process, as result from the online assessment that is performed after each proposal and counter-proposal. The proposed method is integrated in a decision support system for bilateral negotiations, called Decision Support for Energy Contracts Negotiations (DECON), which in turn is integrated with the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM). This integration enables testing and validating the proposed methodology in a realistic market negotiation environment. A case study is presented, demonstrating the advantages of the proposed approach.

System operators have moved towards the integration of renewable resources. However, these resources make network management unstable as they have variations in produced energy. Thus, some strategic plans, like demand response programs, are required to overcome these concerns. This paper develops an aggregator model with a precise vision of the demand response timeline. The model at first discusses the role of an aggregator, and thereafter is presented an innovative approach to how the aggregator deals with short and real-time demand response programs. A case study is developed for the model using real-time simulator and laboratory resources to survey the performance of the model under practical challenges. The real-time simulation uses an OP5600 machine that controls six laboratory resistive loads. Furthermore, the actual consumption profiles are adapted from the loads with a small-time step to precisely survey the behavior of each load. Also, remuneration costs of the event during the case study have been calculated and compared using both actual and simulated demand reduction profiles in the periods prior to event, such as the ramp period.