Adaptive Electricity Scheduling in Microgrids

Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.Comment: 12 pages, extended technical repor

What Ukraine Taught NATO about Hybrid Warfare

Russia’s invasion of Ukraine in 2022 forced the United States and its NATO partners to be confronted with the impact of hybrid warfare far beyond the battlefield. Targeting Europe’s energy security, Russia’s malign influence campaigns and malicious cyber intrusions are affecting global gas prices, driving up food costs, disrupting supply chains and grids, and testing US and Allied military mobility. This study examines how hybrid warfare is being used by NATO’s adversaries, what vulnerabilities in energy security exist across the Alliance, and what mitigation strategies are available to the member states. Cyberattacks targeting the renewable energy landscape during Europe’s green transition are increasing, making it urgent that new tools are developed to protect these emerging technologies. No less significant are the cyber and information operations targeting energy security in Eastern Europe as it seeks to become independent from Russia. Economic coercion is being used against Western and Central Europe to stop gas from flowing. China’s malign investments in Southern and Mediterranean Europe are enabling Beijing to control several NATO member states’ critical energy infrastructure at a critical moment in the global balance of power. What Ukraine Taught NATO about Hybrid Warfare will be an important reference for NATO officials and US installations operating in the European theater.https://press.armywarcollege.edu/monographs/1952/thumbnail.jp

Game theory-based power flow management in a peer-to-peer energy sharing network

In deregulated electricity markets, profit driven electricity retailers compete to supply cheap reliable electricity to electricity consumers, and the electricity consumers have free will to switch between the electricity retailers. The need to maximize the profits of the electricity retailers while minimizing the electricity costs of the electricity consumers has therefore seen a drastic increase in the research of electricity markets. One of the factors that affect the profits of the electricity retailers and the energy cost of the consumers in electricity retail markets is the supply and demand. During high-supply and low-demand periods, the excess electricity if not managed, is wasted. During low-supply high-demand periods, the deficit supply can lead to electricity blackouts or costly electricity because of the volatile electricity wholesale spot market prices. Research studies have shown that electricity retailers can achieve significant profits and reduced electricity costs for their electricity consumers by minimizing the excess electricity and deficit electricity. Existing studies developed load forecasting models that aimed to match electricity supply and electricity demand. These models reached excellent accuracy levels, however due to the high volatility character of load demand and the rise of new electricity consumers, load forecasting alone is unable to mitigate excess and deficit electricity. In other studies, researchers proposed charging the electricity consumers’ batteries with excess electricity during high-supply low-demand periods and supplying their deficit electricity during low-supply high-demand periods. Electricity consumers’ incorporating batteries resulted in minimized excess and deficit electricity, in turn, maximizing the profits for the electricity retailers and minimizing the electricity costs for the electricity consumers. However, the batteries are consumer centric and only provide battery energy for the battery-owned consumer. Electricity consumers without battery energy during low-supply highdemand periods have electricity blackouts or require costly electricity from the electricity wholesale spot market. The peer-to-peer (P2P) energy sharing framework which allows electricity consumers to share their energy resources with one another is a viable solution to allow electricity consumers to share their battery energy. P2P energy sharing is a hot topic in research because of its potential to maximize the electricity retailers’ profits and minimize the electricity consumers’ electricity costs. Due to the increased profits for the electricity retailer and reduced electricity costs for the electricity consumers from implementing battery charging and P2P energy sharing, this dissertation proposes a day-ahead electricity retail market structure in which the electricity retailer supplies consumers’ batteries with excess electricity during high-supply low-demand periods, and during low-supply highdemand periods the electricity retailer discharges the consumers’ batteries to supply their deficit supply or supply their peers’ deficit supply. The electricity retailer aims to maximize its profits and minimize the electricity cost of the electricity consumers in its electricity retail market, by minimizing the excess and deficit electricity. The problem is formulated as a non-linear optimization model and solved using game theory. This dissertation compares the profits of the electricity retailer and electricity costs of the consumers that charge their batteries with excess electricity, discharge their batteries and purchase electricity from their peers to supply their deficit supply, with consumers that only charge their batteries with excess electricity but do not share their battery energy with their peers, consumers that only purchase electricity from their peers to supply their deficit supply but do not employ a battery, and consumers that neither employ a battery nor purchase electricity from their peers to supply their deficit supply. The results show that the consumers that charge their batteries with excess electricity, discharge their batteries and purchase electricity from their peers to supply their deficit supply achieved the lowest electricity cost and highest profits for the electricity retailer.Dissertation (MEng)--University of Pretoria, 2020.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Energy Supplies in the Countries from the Visegrad Group

The purpose of this Special Issue was to collect and present research results and experiences on energy supply in the Visegrad Group countries. This research considers both macroeconomic and microeconomic aspects. It was important to determine how the V4 countries deal with energy management, how they have undergone or are undergoing energy transformation and in what direction they are heading. The articles concerned aspects of the energy balance in the V4 countries compared to the EU, including the production of renewable energy, as well as changes in its individual sectors (transport and food production). The energy efficiency of low-emission vehicles in public transport and goods deliveries are also discussed, as well as the energy efficiency of farms and energy storage facilities and the impact of the energy sector on the quality of the environment

Optimal control in a micro grid of households equipped with μ-CHPs and energy storage devices

This work studies optimal flow control of a micro grid consisting of households equipped with μ-CHP devices and gas and heat buffers. Agricultural wastes from households are used to produce biogas by a biogas generator. The produced biogas is, then, utilized to fulfill local demand of heat and power of the households. Excess biogas can be upgraded and sold to the low pressure gas grid. Excess electricity produced by the μ- CHPs of households can be also sold to the electricity grid. The aim of the control process is to maximize the estimated profit of the households while avoiding overloading gas and electricity grids and avoiding the biogas shortage. The decisions on the supply and consumption levels are done in both centralized and distributed fashions using model predictive control (MPC). The distributed MPC (dMPC) is developed from the centralized MPC (cMPC) by employing dual decomposition method combined with the projected sub-gradient method. In dMPC, each household makes decisions based on its local information, yet still needs to coordinate its supply and consumption bids to the grid operators and the biogas generator. The coordinations are formulated for synchronous and asynchronous implementations. With the distributed scheme, the grid operators and the biogas producer can manage households’ supply and consumption levels via dynamic pricing to obey the grid capacity constraints. We perform extensive simulations to investigate the behavior of dynamic pricing modified by the grid operators and the biogas generator. Furthermore, we provide numerical results to compare the performance of cMPC, synchronous dMPC, and asynchronous dMPC using realistic estimates of the selling prices and demand patterns

Optimization and Learning Methods for Electric Distribution Network Management

University of Minnesota Ph.D. dissertation.June 2019. Major: Electrical Engineering. Advisor: Nicholas Sidiropoulos. 1 computer file (PDF); ix, 108 pages.Distribution system state estimation (DSSE) is a core task for monitoring and control of distribution networks. Widely used Gauss-Newton approaches are not suitable for real-time estimation, often require many iterations to obtain reasonable results, and sometimes fail to converge. Learning-based approaches hold the promise for accurate real-time estimation. This dissertation presents the first data-driven approach to `learn to initialize' -- that is, map the available measurements to a point in the neighborhood of the true latent states (network voltages), which is used to initialize Gauss-Newton. In addition, a novel learning model is also presented that utilizes the electrical network structure. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. The proposed approach is the first that leverages electrical laws and grid topology to design the neural network for DSSE. It is shown that the proposed approaches yield superior performance in terms of stability, accuracy, and runtime, compared to conventional optimization-based solvers. The second part of the dissertation focuses on the AC Optimal Power Flow (OPF) problem for multi-phase systems. Particular emphasis is given to systems with large-scale integration of renewables, where adjustments of real and reactive output power from renewable sources of energy are necessary in order to enforce voltage regulation. The AC OPF problem is known to be nonconvex (and, in fact, NP-hard). Convex relaxation techniques have been recently explored to solve the OPF task with reduced computational burden; however, sufficient conditions for tightness of these relaxations are only available for restricted classes of system topologies and problem setups. Identifying feasible power-flow solutions remains hard in more general problem formulations, especially in unbalanced multi-phase systems with renewables. To identify feasible and optimal AC OPF solutions in challenging scenarios where existing methods may fail, this dissertation leverages the Feasible Point Pursuit - Successive Convex Approximation algorithm – a powerful approach for general nonconvex quadratically constrained quadratic programs. The merits of the approach are illustrated using several multi-phase distribution networks with renewables

Renewable Energy

The demand for secure, affordable and clean energy is a priority call to humanity. Challenges associated with conventional energy resources, such as depletion of fossil fuels, high costs and associated greenhouse gas emissions, have stimulated interests in renewable energy resources. For instance, there have been clear gaps and rushed thoughts about replacing fossil-fuel driven engines with electric vehicles without long-term plans for energy security and recycling approaches. This book aims to provide a clear vision to scientists, industrialists and policy makers on renewable energy resources, predicted challenges and emerging applications. It can be used to help produce new technologies for sustainable, connected and harvested energy. A clear response to economic growth and clean environment demands is also illustrated

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book