Research on grid challenges and smart grid development: the case of Sichuan grid

As the most important driving force of modern social development and a significant symbol of modern civilization, electric power is in booming demand. Furthermore, electric power is a complex system which integrates power generation, power transmission, power distribution and power utilization together and achieves generation, transmission, distribution and utilization instantaneously at the same time. It notably features with network industry and network economy. Power grid is a hub which links electricity production and electricity consumption in the power system. On the basis of basic theories of network industry and network economy, this thesis discusses the development of smart grid from the aspects of “network challenges”, resources and energy challenges and new energy access challenges encountered and counter-measures in the development of modern grid. Based on the development environment of China power, especially the Sichuan power grid, and spatial mismatching of power supply and demand (including new energy resources and distribution), this thesis analyzes and explains China (Sichuan) smart grid is strong smart grid which has UHV power grid as the backbone frame, and features information technology, and automation.Devido ao facto de ser uma força impulsionadora do desenvolvimento económico e um simbolo muito importante da civilização moderna, a procura de electricidade tem aumentado consideravelmente nas últimas décadas. Contudo, a energia elétrica é um sistema complexo que integra geração, transmissão, distribuição e implica que a oferta e a procura sejam simultâneas. A indústria da electricidade tem muitas características da economia em rede. A rede elétrica deve ser vista como um “hub” que liga a produção de electricidade ao seu consumo. Tendo por base, as teorias da indústria em rede e da economia em rede, esta tese discute o desenvolvimento das redes elétricas segundo as perspectivas dos “desafios que se colocam às redes”, dos desafios em termos de recursos e dos desafios que se colocam ao desenvolvimento da rede elétrica moderna. Esta tese estuda de uma forma detalhada os problemas relacionados com a construção de uma rede elétrica inteligente na província de Sichuan, China

Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

Solar Thermal Plants Integration in Smart Grids

Solar energy penetration has been increasingly growing in recent years. Since solar energy is intermittent its integration in existing grids is difficult. This paper deals with the optimal integration of solar power plants in grids. The paper proposes a modification of energy hubs which allows to solve the optimization problem with a mixed integer programming algorithm in a distributed way. An introductory simulation study case is givenMinisterio de Educación DPI2008-05818Junta de Andalucía P07-TEP-02720Comisión Europea HD-MP

A new method to energy saving in a micro grid

Optimization of energy production systems is a relevant issue that must be considered in order to follow the fossil fuels consumption reduction policies and CO2 emission regulation. Increasing electricity production from renewable resources (e.g., photovoltaic systems and wind farms) is desirable but its unpredictability is a cause of problems for the main grid stability. A system with multiple energy sources represents an efficient solution, by realizing an interface among renewable energy sources, energy storage systems, and conventional power generators. Direct consequences of multi-energy systems are a wider energy flexibility and benefits for the electric grid, the purpose of this paper is to propose the best technology combination for electricity generation from a mix of renewable energy resources to satisfy the electrical needs. The paper identifies the optimal off-grid option and compares this with conventional grid extension, through the use of HOMER software. The solution obtained shows that a hybrid combination of renewable energy generators at an off-grid location can be a cost-effective alternative to grid extension and it is sustainable, techno-economically viable, and environmentally sound. The results show how this innovative energetic approach can provide a cost reduction in power supply and energy fees of 40% and 25%, respectively, and CO2 emission decrease attained around 18%. Furthermore, the multi-energy system taken as the case study has been optimized through the utilization of three different type of energy storage (Pb-Ac batteries, flywheels, and micro—Compressed Air Energy Storage (C.A.E.S.)

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Load control in low voltage level of the electricity grid using µCHP appliances

The introduction of microCHP (Combined Heat and Power) appliances and other means of distributed generation causes a shift in the way electricity is produced and consumed. Households themselves produce electricity and deliver the surplus to the grid. In this way, the distributed generation also has implications on the transformers and, thus, on the grid. In this work we study the influence of introducing microCHP appliances on the total load of a group of houses (behind the last transformer). If this load can be controlled, the transformer may be relieved from peak loads. Moreover, a well controlled fleet production can be offered as a Virtual Power Plant to the electricity grid.\ud \ud In this work we focus on different algorithms to control the fleet and produce a constant electricity output. We assume that produced electricity is consumed as locally as possible (preferably within the household). Produced heat can only be consumed locally. Additionally, heat can be stored in heat stores. Fleet control is achieved by using heat led control algorithms and by specifying as objective how much of the microCHP appliances have to run.\ud \ud First results show that preferred patterns can be produced by using fleet control. However, as the problem is heat driven, still reasonably large deviations from the objective occur. Several combinations of heat store and fleet control algorithm parameters are considered to match the heat demand and supply.\ud \ud This work is a first attempt in controlling a fleet and gives a starting point for further research in this area. A certain degree of control can already be established, but for better stability more intelligent algorithms are needed