Use of Hooke's law for stabilizing future smart grid - the electric spring concept

Hooke's law for mechanical springs was developed in the 17th century. Recently, new power electronics devices named electric springs have been developed for providing voltage regulation for distribution networks and allowing the load demand to follow power generation. This paper summarizes recent R&D on electric springs and their potential functions for future smart grid. Electric springs can be associated with electric appliances, forming a new generation of smart loads which can adapt according to the availability of power from renewable energy sources. When massively distributed over the power grid, they could provide highly distributed and robust support for the smart grid, similar to the arrays of mechanical springs supporting a mattress. Thus, the 3-century old Hooke's law in fact provides a powerful solution to solving some key Smart Grid problems in the 21st Century. © 2013 IEEE.published_or_final_versio

Developing a method to accurately estimate the electricity cost of grid-connected solar PV in Doha

Solar photovoltaic electricity is more expensive compared with conventional electricity at retail level. As a result, general public members do not find the solar PV electricity an attractive option to use for generating a portion of their electricity need. To promote PV electricity utilization and to make it more attractive, Governments of some countries like Germany, Japan, USA, Australia, and etc. have introduced solar PV incentive programs. Most of grid-connected photovoltaic (PV) systems on residential or commercial buildings in these countries are installed by individuals interested in generating part of their electricity emissionfree. For some of these people the economics of the PV electricity is likely to be of secondary importance, while majority of them would like to see financial return to become interested to use PV electricity. The objective of this paper is to present the results of a study conducted on the economic aspects of solar PV to estimate the electricity price of grid-connected rooftop PV system under climate conditions and geographical location of Doha, to see if the use of PV electricity is attractive and affordable by residential customers. The results of this study will help to determining an appropriate feed-in tariff for solar PV electricity in Doha

Sustainable power supply using solar energy and wind power combined with energy storage

The idea of integrating intermittent sources of energy such as solar and wind with energy storage has several benefits for the electricity grid. The first benefit is that energy storage can help the grid during the periods that grid is facing high peak demand. The second benefit is that using energy storage would help shifting the grid load from peak and busy time to a less demand time. And the third benefit is that using energy storage would help smoothing the variations in power generation fed into the grid by variable and intermittent renewable resources. The third benefit is of particular important because in future more renewable energy sources will be integrated into the electricity grid worldwide. The objective of this paper is to present the results of a study conducted to examine the potential role and potential benefits of energy storage integrated into intermittent sources. Using energy storage will provide an opportunity to create a sustainable power supply, and to make the electricity grid more reliable especially with large proportion of grid-connected renewable sources

A Review on Equipment Protection and System Protection Relay in Power System

Power system equipment is configured and connected together with multiple voltage levels in existing electrical power system. There are varieties of electrical equipment obtainable in the power system predominantly from generation side up to the distribution side. Consequently, appropriate protections must be apt to prevent inessential disturbances that lead to voltage instability, voltage collapse and sooner a total blackout took place in the power system. The understanding of each component on the system protection is critical. This is due to any abnormal condition and failure can be analyzed and solved effectively due to the rapid changing and development on the power system network. Therefore, the enhancement of power quality can be achieved by sheltering the equipment with protection relay in power system. Moreover, the design of a systematic network is crucial for the system protection itself. Several types of protective equipment and protection techniques are taken into consideration in this paper. Hence, the existing accessible types and methods of system protection in the power system network are reviewed

On the Scaling Property of Power Grids

Compared with other natural or man-made networks, electric power grid assumes distinct electric topology with special small-world properties and electrical parameter settings. In this paper we study the scaling property of power grid in terms of both topology measures and electric parameters, with a number of realistic power grid test cases of different size. The examined measures and parameters include average node degree, average path length, algebraic connectivity, the bus type entropy that characterize relative locations of generation and load buses, generation capacity, total demand, and transmission capacity. Interpreting and testing the scaling property of power grid will help us better understand the intrinsic characteristics of electric energy delivery network of this critical infrastructure; and enable the development of an appropriate synthetic modeling that could be utilized to generate power grid test cases with accurate grid topology and electric parameters

Reward/Penalty Design in Demand Response for Mitigating Overgeneration Considering the Benefits from Both Manufacturers and Utility Company

The high penetration of renewable sources in electricity grid has led to significant economic, environmental, and societal benefits. However, one major side effect, overgeneration, due to the uncontrollable property of renewable sources has also emerged, which becomes one of the major challenges that impedes the further large-scale adoption of renewable technology. Electricity demand response is an effective tool that can balance the supply and demand of the electricity throughout the grid. In this paper, we focus on the design of reward/penalty mechanism for the demand response programs aiming to mitigate the overgeneration. The benefits for both manufacturers and utility companies are formulated as the function of reward and penalty. The formulation is solved using particle swarm optimization so that the benefit from both supply side can be maximized under the constraint the benefit of customer side is not sacrificed. A numerical case study is used to verify the effectiveness of the proposed method

Optimizing Electricity Load and Cost for Demand Side Management in Smart Grid

This paper proposes a mechanism for OELC (Optimizing Electricity Load and Cost) for smart grid. The load of every smart home is predicted one-hour prior to their actual usage. To fulfill PL (Predicted Load) of each consumer, multiple resources of electricity are considered, including RE (Renewable Energy) resources. Furthermore, cost to get PL from multiple resources is calculated. In proposed model 3-4 smart homes are grouped in the form of clusters. To reduce the amount of electricity bills, system also allows privileges to share electricity between adjacent smart homes within a cluster. To validate the OELC mechanism, extensive numerical simulations are conducted which shows a significant reduction in electricity load and cost for electricity consumers. In future, to enhance the functionality of OELC, security from cyber-attacks can be considere

Smart grid framework analysis and artificial neutral network in load forecast

Power system is the one of the most critical parts of the whole energy utilization around the world. Recently people pay more attention to the energy utilization, new types of generations, storages and power utilization need to increase energy efficiency and reduce carbon emission. Due to the power grid currently is still mainly under the old-designed approach, it is increasingly exposed limitation on efficiency enhancement, security and reliability improvement, new technologies compatibility and meeting larger power capacity requirements. Thus, Smart Grid is 'born' to improve power grid for these requirements. It is an overlapping area between power system and digital technology, intelligent technology, communication technology and so on. Smart Grid can provide updates for nearly all sections of traditional power grid. It is a systematic framework that new technologies integration, system development strategy and planning, customers' awareness improvements and supports from all relevant areas. The areas must be operated in coordination and parallel. Firstly, this thesis introduces Smart Grid and Smart Metering on its definition, characteristics and deployment. Secondly, this thesis describes a load forecasting system for macro-grid. Artificial Neural Network (ANN) was introduced to achieve this work for its excellent mapping approximation ability. In the third section, thesis focuses on load forecasting for micro-grid. BackPropagation method is used to train the Multi-layer Perceptron (MLP) ANN and its results were compared to that from Radial Basis Function (RBF) ANN. Analysis was focused not only on the two networks but also ANN generalization problems and differences between micro-grid load and macro-grid load prediction

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches

Controlo preditivo distribuído em edifícios para conforto térmico e alocação de cargas

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica IndustrialA Gestão da Procura ou Demand-Side Management (DSM) de energia nos edifícios tem por base um aumento na eficiência na utilização de energia através da alteração da forma e da amplitude do diagrama de carga do consumidor. O DSM envolve uma combinação de estratégias como o armazenamento de energia, controlo de temperaturas, alocação de cargas e o alisamento de picos de consumo. Nesta dissertação estudam-se metodologias de controlo preditivo distribuído do tipo Modelo de Controlo Preditivo (MPC) que possam ser aplicadas ao controlo de um sistema de climatização em edifícios e que permitam outras cargas (como por exemplo carregamento de um carro eléctrico) se conectem à rede na hora mais favorável para o consumidor. O algoritmo proposto é uma metodologia de controlo preditivo distribuído especificamente para edifícios com diferentes características e com diferentes necessidades térmicas e de cargas. Os edifícios partilham um recurso energético renovável limitado e, simultaneamente, procuram satisfazer as suas exigências de conforto térmico e obter o perfil de consumo mais adequado às suas necessidades. A abordagem foi testada e validada com vários cenários. Os resultados obtidos demonstram que a utilização do controlo preditivo distribuído num contexto de DSM são uma estratégia válida que possibilita obter conforto térmico e alocação de cargas permitindo uma redução de consumo e de custos.Abstract: The Demand Side Management (DSM) in buildings aims to increase the energy usage efficiency by reducing the peak loads and changing the consumer load profile. DSM promotes the energy savings through a combination of different strategies like thermal control, peak clipping, load shifting and energy storage. In this thesis, a distributed predictive control methodology is studied, MPC (Model Predictive Control) is applied to an air conditioning control system in buildings to provide thermal comfort allowing also that other appliances (eg charging an electric car) may be connected to the network in the most favorable time by load shifting. The proposed algorithm is a customized distributed predictive control methodology for buildings with different characteristics and different thermal needs and loads. Buildings also share a limited green energy resource and must fulfill their indoor comfort constraints along with the best load profile. The validity of the approach was tested with several scenarios. Results show that the distributed model predictive control strategy provides indoor comfort and load shifting in DSM context providing a valid methodology to achieve less consumption and price reduction