A Holistic Approach to Forecasting Wholesale Energy Market Prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions on Power System

A holistic approach to forecasting wholesale energy market prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict th

Integration of Real-Intelligence in Energy Management Systems to Enable Holistic Demand Response Optimization in Buildings and Districts

Although multiple trials have been conducted demonstrating that demand side flexibility works and even though technology roll-out progresses significantly fast, the business application of residential and small tertiary demand response has been slow to develop. This paper introduces a holistic demand response optimization framework that enables significant energy costs reduction at the consumer side, while introducing buildings as a major contributor to energy networks' stability in response to network constraints and conditions. The backbone of the solution consists in a modular interoperability and data management framework that enables open standards-based communication along the demand response value chain. The solution is validated in four large-scale pilot sites, incorporating diverse building types, heterogeneous home, building and district energy systems and devices, a variety of energy carriers and spanning diverse climatic conditions, demographic and cultural characteristics.European Commission's H2020, 76861

Реструктуризація ринку електроенергії в контексті трансформаційних процесів і ціноутворення

The study introduces several most significant empirical and analytical findings covering the issue of electricity restructuring. In this regard, it detailes the dataset on electricity framework in order to investigate tendencies and the main challenges in the market structure following the regulation and deregulation processes of the electricity sector, as well as the consequences for pricing. The purpose of the study is to analyze key aspects of the restructuring of electric energy markets in order to determine the main challenges related to justification of the optimal structure of the industry with this framework, as well as to establish the dependence between reforms, structural transformations and price volatility in the energy market. It should be noted that deregulation and restructuring of energy companies have been determined by the transition to competitive relations and made it possible to solve significant problems in different fields of activity, including tariffs, participants’ interests, energy efficiency, etc. Consequently, reforming of the energy market structural components should be comprehensively implemented, in order to avoid fragmentary imbalances and the impact of price distortions on the participants of the electricity market.Дослідження спрямовано на формування ключових практичних і аналітичних висновків, що стосуються проблем реструктуризації електроенергетики. Для цього виявлено найбільш значущі структурні особливості функціонування електроенергетичного ринку, що дозволило висвітлити деякі існуючі тенденції і основні проблеми в структурі енергоринку, пов'язані з процесами регулювання і дерегулювання, а також проаналізувати вплив трансформаційних процесів на встановлення цін на електроенергію, на прикладі ринку електричної енергії України, що перебуває у стадії реформування. Результати дослідження отримані із застосуванням методів порівняння, ретроспективного і аналізу, методів узагальнення і систематизації, а також методів кореляційного і регресійного аналізу. Проведений аналіз предмета дослідження дозволяє зробити висновок, що дерегулювання і реструктуризація енергетичних компаній були визначені в якості переходу до конкурентних відносин і дозволили вирішити значні проблеми в різних сферах господарювання. Отже, реформа структурних складових енергоринку має здійснюватися комплексно, для уникнення фрагментарних диспропорцій і впливу цінових спотворень для функціонування учасників ринку електроенергії

East Midlands food & drink sector strategy

The food & drink sector is one of four priority sectors identified in the regional economic strategy, A Flourishing Region. This document sets out a strategy for maximising the contribution of the food & drink sector to the economic development of the East Midlands

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

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