Enhancing Smart GRIDs for Sustainability

Funder

Organizational Unit

Publications

Book of Abstracts - International Congress on Organizational Management, Energy Efficiency and Occupational Health and Safety in Agrifood Industry (+AGRO 2018)

Publication . Gaspar, Pedro Dinis

Congress +AGRO is a technical and scientific international event devoted to food science and technology. It brings together researchers, scientists, policy makers, professionals and students from multidisciplinary food-related fields to share the latest advances in the current scientific knowledge, with industrial relevance and new developments in food science and emerging technologies. It aims to discuss innovations either in the approach or in the methods used along the food processing chain, as well as promising food processing technologies, which are significant for the science community or for the food industry. Communications addressing the novel combination of more than one technology are welcome, as well as studies dealing with innovation and advances in all branches of food science. See all details at https://congresso.maisagro.pt/en/home-en/

2018Book

Open access

Investigation on electricity market designs enabling demand response and wind generation

Publication . Hajibandeh, Neda; Catalão, João Paulo da Silva; Mariano, Sílvio José Pinto Simões; Shafie-khah, Miadreza

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.

2019-06Doctoral thesis

Open access

Control of modular multilevel converters in high voltage direct current power systems

Publication . Mehrasa, Majid; Catalão, João Paulo da Silva; Calado, Maria do Rosário Alves; Pouresmaeil, Edris

This thesis focuses on a comprehensive analysis of Modular Multilevel Converters (MMC) in High Voltage Direct Current (HVDC) applications from the viewpoint of presenting new mathematical dynamic models and designing novel control strategies. In the first step, two new mathematical dynamic models using differential flatness theory (DFT) and circulating currents components are introduced. Moreover, detailed step-by-step analysis-based relationships are achieved for accurate control of MMCs in both inverter and rectifier operating modes. After presenting these new mathematical equations-based descriptions of MMCs, suitable control techniques are designed in the next step. Because of the nonlinearity features of MMCs, two nonlinear control strategies based on direct Lyapunov method (DLM) and passivity theory-based controller combined with sliding mode surface are designed by the use of circulating currents componentsbased dynamic model to provide a stable operation of MMCs in HVDC applications under various operating conditions. The negative effects of the input disturbance, model errors and system uncertainties are suppressed by defining a Lyapunov control function to reach the integralproportional terms of the flat output errors that should be finally added to the initial inputs. Simulation results in MATLAB/SIMULINK environment verify the positive effects of the proposed dynamic models and control strategies in all operating conditions of the MMCs in inverter mode, rectifier mode and HVDC applications.

2019-06Doctoral thesis

Open access

Stochastic management framework of distribution network systems featuring large-scale variable renewable energy sources and flexibility options

Publication . Cruz, Marco Rafael Meneses; Catalão, João Paulo da Silva; Mariano, Sílvio José Pinto Simões; Fitiwi, Desta Zahlay

The concerns surrounding climate change, energy supply security and the growing demand are forcing changes in the way distribution network systems are planned and operated, especially considering the need to accommodate large-scale integration of variable renewable energy sources (vRESs). An increased level of vRESs creates technical challenges in the system, bringing a huge concern for distribution system operators who are given the mandate to keep the integrity and stability of the system, as well as the quality of power delivered to end-users. Hence, existing electric energy systems need to go through an eminent transformation process so that current limitations are significantly alleviated or even avoided, leading to the so-called smart grids paradigm. For distribution networks, new and emerging flexibility options pertaining to the generation, demand and network sides need to be deployed for these systems to accommodate large quantities of variable energy sources, ensuring an optimal operation. Therefore, the management of different flexibility options needs to be carefully handled, minimizing the sideeffects such as increasing costs, worsening voltage profile and overall system performance. From this perspective, it is necessary to understand how a distribution network can be optimally operated when featuring large-scale vRESs. Because of the variability and uncertainty pertinent to these technologies, new methodologies and computational tools need to be developed to deal with the ensuing challenges. To this end, it is necessary to explore emerging and existing flexibility options that need to be deployed in distribution networks so that the uncertainty and variability of vRESs are effectively managed, leading to the real-time balancing of demand and supply. This thesis presents an extensive analysis of the main technologies that can provide flexibility to the electric energy systems. Their individual or collective contributions to the optimal operation of distribution systems featuring large-scale vRESs are thoroughly investigated. This is accomplished by taking into account the stochastic nature of intermittent power sources and other sources of uncertainty. In addition, this work encompasses a detailed operational analysis of distribution systems from the context of creating a sustainable energy future. The roles of different flexibility options are analyzed in such a way that a major percentage of load is met by variable RESs, while maintaining the reliability, stability and efficiency of the system. Therefore, new methodologies and computational tools are developed in a stochastic programming framework so as to model the inherent variability and uncertainty of wind and solar power generation. The developed models are of integer-mixed linear programming type, ensuring tractability and optimality.

2019-06Doctoral thesis

Open access

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Publication . Talari, Saber; Catalão, João Paulo da Silva; Gaspar, Pedro Miguel de Figueiredo Dinis Oliveira; Shafie-khah, Miadreza

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.

2019-04Doctoral thesis

Open access