Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

Decentralized Multi-Agent System Applied to the Decision Making Process of the Microgrid Restoration Procedure towards Sustainability

A significant procedure to ensure the consumer supply is Power System Restoration (PSR). Due to the increase of the number of distributed generators in the grid, it is possible to shift from the conventional PSR to a new strategy involving the use of distributed energy resources (DER). In this paper, a decentralized multi-agent system (MAS) is proposed to cope with the restoration procedure in a microgrid (MG). Each agent is assigned to a specific consumer or microsource (MS), communicating with other agents at every stage of the restoration procedure so that a common decision is reached. The 0/1 knapsack problem is the problem that every agent solves to determine the best load connection sequence during the restoration of the MG. Two different case studies are used to test the MAS on a dynamically modeled benchmark MG: a total blackout and a partial blackout. Regarding the partial blackout case, demand response emergency programs are considered to manage the loads in the MG. The MAS is developed in Matlab/Simulink environment and by performing the corresponding dynamic simulations it is possible to validate this system towards sustainability

Stochastic Security Constrained Unit Commitment with High Penetration of Wind Farms

Secure and reliable operation is one of the main challenges in restructured power systems. Wind energy has been gaining increasing global attention as a clean and economic energy source, despite the operational challenges its intermittency brings. In this study, we present a formulation for electricity and reserve market clearance in the presence of wind farms. Uncertainties associated with generation and line outages are modeled as different system scenarios. The formulation incorporates the cost of different scenarios in a two-stage short-term (24-hours) clearing process, also considering different types of reserve. The model is then linearized in order to be compatible with standard mixed-integer linear programming solvers, aiming at solving the security constrained unit-commitment problem using as few variables and optimization constraints as possible. As shown, this will expedite the solution of the optimization problem. The model is validated by testing it on a case study based on the IEEE RTS1, for which results are presented and discussed.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Analysis Application of Controllable Load Appliances Management in a Smart Home

The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.fi=vertaisarvioitu|en=peerReviewed

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

Insular power systems represent an asset and an excellent starting point for the development and analysis of innovative tools and technologies. The integration of renewable energy resources that has taken place in several islands in the south of Europe, particularly in Portugal, has brought more uncertainty to production management. In this work, an innovative scheduling model is proposed, which considers the integration of wind and solar resources in an insular power system in Portugal, with a strong conventional generation basis. This study aims to show the benefits of increasing the integration of renewable energy resources in this insular power system, and the objectives are related to minimizing the time for which conventional generation is in operation, maximizing profits, reducing production costs, and consequently, reducing greenhouse gas emissions

Nova metodologia híbrida para a previsão dos preços da energia eléctrica e da potência eólica a curto prazo

A implementação de um mercado eléctrico liberalizado e a crescente integração de energia eólica na rede eléctrica, particularmente em Portugal, induzem novos desafios associados à crescente competitividade no sector eléctrico entre empresas produtoras e à elevada volatilidade e intermitência inerentes ao vento. Assim, torna-se indispensável para os agentes de mercado a existência de ferramentas computacionais mais eficientes que permitam obter previsões fiáveis e rigorosas dos preços da energia eléctrica e da potência eólica. Estas previsões possibilitam desenvolver melhores estratégias de oferta no mercado, maximizando o lucro, e optimizando a exploração dos recursos energéticos de origem eólica. Esta dissertação apresenta uma nova metodologia híbrida para a previsão dos preços da energia eléctrica e da potência eólica em Portugal, considerando o horizonte temporal de curto prazo, isto é, de um dia a uma semana. Esta nova metodologia baseia-se na combinação eficaz de sistemas neuro-difusos, programação evolucionária e optimização por enxame de partículas, sendo aplicada em casos de estudo reais. Os resultados obtidos são posteriormente comparados com resultados já publicados em revistas internacionais de referência, permitindo validar a proficiência da nova metodologia proposta

A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation

A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

Insular power systems represent an asset and an excellent starting point for the development and analysis of innovative tools and technologies. The integration of renewable energy resources that has taken place in several islands in the south of Europe, particularly in Portugal, has brought more uncertainty to production management. In this work, an innovative scheduling model is proposed, which considers the integration of wind and solar resources in an insular power system in Portugal, with a strong conventional generation basis. This study aims to show the benefits of increasing the integration of renewable energy resources in this insular power system, and the objectives are related to minimizing the time for which conventional generation is in operation, maximizing profits, reducing production costs, and consequently, reducing greenhouse gas emissions

Optimal planning of CHP-based microgrids considering DERs and demand response programs

This work addresses a stochastic framework for optimal operation and long-term expansion planning of combined heat and power based microgrid as a part of an active distributing system. The microgrid utilizes renewable energy sources, electricity and heat generation units, energy storage systems, and demand response programs. The proposed model determines the optimal location and capacity of the electrical and thermal facilities, and it considers the impact of renewable energy sources and demand response on the expansion-planning problem. A stochastic mixed-integer linear programming formulation is utilized to minimize the investment and operation costs of system for five years. To evaluate the effectiveness of the proposed model, the algorithm is assessed for the 9-bus system and the 33-bus IEEE test systems. The results demonstrate that the utilization of the proposed algorithm reduces the operational cost and increases system revenues.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Flexible co-operation of TCSC and corrective topology control under wind uncertainty : an interval-based robust approach

This paper presents an AC optimal power flow (AC-OPF) model including flexible resources (FRs) to handle uncertain wind power generation (WPG). The FRs considered are thermal units with up/down re-dispatching capability, corrective topology control (CTC), and thyristor-controlled series capacitors (TCSC). WPG uncertainty has been modeled through a proposed interval-based robust approach, the goal of which is to maximize the variation range of WPG uncertainty in power systems while maintaining an adequate reliability level at a reasonable cost with the aid of FRs. However, utilization of FRs (especially CTC and TCSC devices) is limited due to the difficulty of their incorporation in the AC-OPF. The optimization framework of the full FR-augmented AC-OPF problem is a mixed-integer nonlinear programming (MINLP) in which the solution for large-scale systems is very hard to obtain. To solve this issue, this paper uses a two-stage decomposition algorithm to decompose the MINLP representation into a mixed-integer linear program (MILP) and a nonlinear program (NLP). Finally, the robust AC-OPF model with FRs is implemented and tested on a 6-bus and the IEEE 118-bus test systems to evaluate its efficiency and performance.fi=vertaisarvioitu|en=peerReviewed