CleanTX Analysis on the Smart Grid

The utility industry in the United States has an opportunity to revolutionize its electric grid system by utilizing emerging software, hardware and wireless technologies and renewable energy sources. As electricity generation in the U.S. increases by over 30% from today’s generation of 4,100 Terawatt hours per year to a production of 5,400 Terawatt hours per year by 2030, a new type of grid is necessary to ensure reliable and quality power. The projected U.S. population increase and economic growth will require a grid that can transmit and distribute significantly more power than it does today. Known as a Smart Grid, this system enables two- way transmission of electrons and information to create a demand-response system that will optimize electricity delivery to consumers. This paper outlines the issues with the current grid infrastructure, discusses the economic advantages of the Smart Grid for both consumers and utilities, and examines the emerging technologies that will enable cleaner, more efficient and cost- effective power transmission and consumption.IC2 Institut

Econometric framework for electricity infrastructure modernization in Saudi Arabia, An

2017 Summer.Includes bibliographical references.The electricity infrastructure in Saudi Arabia is facing several challenges represented by demand growth, high peak demand, high level of government subsidies, and system losses. This dissertation aims at addressing these challenges and proposing a multi-dimensional framework to modernize the electricity infrastructure in Saudi Arabia. The framework proposes four different scenarios—identified by two dimensions—for the future electric grid. The first and second dimensions are characterized by electricity market deregulation and Smart Grid technologies (SGTs) penetration, respectively. The framework analysis estimates global welfare (GW) and economic feasibility of the two dimensions. The first dimension quantifies the impact of deregulating the electricity market in Saudi Arabia. A non-linear programming (NLP) algorithm optimizes consumers surplus, producers surplus, and GW. The model indicates that deregulating the electricity market in Saudi Arabia will improve market efficiency. The second dimension proposes that allowing the penetration of SGTs in the Saudi electricity infrastructure is expected to mitigate the technical challenges faced by the grid. The dissertation examines the priorities of technologies for penetration by considering some key performance indicators (KPIs) identified by the Saudi National Transformation Program, and Saudi Vision 2030. A multi-criteria decision making (MCDM) algorithm—using the fuzzy Analytic Hierarchy Process (AHP)—evaluates the prioritization of SGTs to the Saudi grid. The algorithm demonstrates the use of triangular fuzzy numbers to model uncertainty in planning decisions. The results show that advanced metering infrastructure (AMI) technologies are the top priority for modernizing the Saudi electricity infrastructure; this is followed by advanced assets management (AAM) technologies, advanced transmission operations (ATO) technologies, and advanced distribution operations (ADO) technologies. SGTs prioritization is followed by a detailed cost benefit analysis (CBA) conducted for each technology. The framework analysis aims at computing the economic feasibility of SGTs and estimating their outcomes and impacts in monetary values. The framework maps Smart Grid assets to their functions and benefits to estimate the feasibility of each Smart Grid technology and infrastructure. Discounted cash flow (DCF) and net present value (NPV) models, benefit/cost ratio, and minimum total cost are included in the analysis. The results show that AAM technologies are the most profitable technologies of Smart Grid to the Saudi electricity infrastructure, followed by ADO technologies, ATO technologies, and AMI technologies. Considering the weights resulting from the fuzzy AHP and the economic analysis models for each infrastructure, the overall ranking places AAM technologies as the top priority of SGTs to the Saudi electricity infrastructure, followed by AMI technologies, ADO technologies, and ATO technologies. This dissertation has contributed to the existing body of knowledge in the following areas: • Proposing an econometric framework for electricity infrastructure modernization. The framework takes into account technical, economic, environmental, societal, and policy factors. • Building an NLP algorithm to optimize a counterfactual deregulation of a regulated electricity market. The algorithm comprises short run price elasticity of electricity demand (ε), level of technical efficiency improvement, and discount rate (r). • Proposing an MCDM model using AHP and fuzzy set theory to prioritize SGTs to electricity infrastructures. • Adapting a Smart Grid asset-function-benefit linkage model that maps SGTs to their respected benefits. • Conducting a detailed CBA to estimate the economic feasibility of SGTs to the Saudi electricity infrastructure, This work opens avenues for more analysis on electricity infrastructure modernization. Measuring risk impact and likelihood is one area for future research. In fact, risk assessment is an important factor in determining the economic feasibility of the modernization. Probabilistic economic analysis can be applied to assess the risk associated with the implantation of the previously mentioned dimensions. The parameters used for the economic analysis, such as economic life of a project, and the discount rate, are usually deterministic. However, a probabilistic method can be applied to capture the uncertainty of the parameters. Another area for future research is the integration of both dimensions into one model in which GW resulted from market deregulation and SGTs insertion are summed

From a Barrier to a Bridge: Data-Privacy in Deregulated Smart Grids

The introduction of so-called smart meters involves detailed consumption data. While this data plays a key role in integrating volatile renewable energy sources, a side effect is that it can reveal sensitive personal information. Concerns and protests led to a stopped smart meter rollout yet. In deregulated electricity markets, data-privacy is even more at risk: The UK, Texas and Ontario decided for a nation-wide communication intermediary in order to facilitate the exchange of the vast amount of smart meter data. However, this operational efficiency is achieved by the fact that an intermediary is a single point of failure. We present an approach based on encryption to secure the intermediary against privacy invasions and we can show that our prototypical implementation meets even restrictive requirements for large-scale data handling and processing. By aiming at customers’ confidence in smart metering, our solution might lay the ground for an ecosystem of energy services

A Framework of Integrating Manufacturing Plants in Smart Grid Operation: Manufacturing Flexible Load Identification

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation may occur in the real-time market and the dispatching plan needs to be adjusted to respond to the variation. To reduce the gap between the day-ahead and real-time dispatching plans, a modified framework, i.e., a three-settlement process considering the integration of the manufacturing plants into the existing two-settlement process is proposed in this study. The manufacturing end-use customers report the flexibility of their loads to the ISO so that the ISO can update the day-ahead price through an updated dispatching plan that utilizes the feedback of the load flexibility from the manufacturers. A mathematical model is developed to identify the flexible and non-flexible loads of the manufacturers. Particle Swarm Optimization (PSO) is used to solve this mathematical model and a case study is conducted to illustrate the effectiveness of the model

Demand Response in Smart Grids: Research Opportunities for the IS Discipline

Information systems are going to play a key role in making today’s electricity grids smarter and, in this way, enabling the energy turnaround from fossil to renewable energy sources. One of the approaches in order to cope with the intermittent feed-in of renewable energy is demand response, i.e. the demand side responds to requirements and price signals from the electricity grid. Until now, the supply side still largely responds to the demand by trying to generate the electricity that is currently needed. As the huge coordination task of demand response in smart grids necessarily requires information systems, this contribution analyzes existing information systems (IS) research contributions on demand response. Subsequently, we identify underrepresented areas by contrasting our findings with the research of related disciplines and research projects funded by the European Union (EU). Finally, we discuss selected open questions and conclude by illustrating what the identified white spots mean for the Smart Grid’s success and provides guidance for future IS research and development

Why Do Electricity Policy and Competitive Markets Fail to Use Advanced PV Systems to Improve Distribution Power Quality?

The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG) exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the network characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. We discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control

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