A Vision for Co-optimized T&D System Interaction with Renewables and Demand Response

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. The use of demand response to counterbalance the intermittency of renewable generation brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. In this paper, a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system is described. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the microgrid can also sell (buy) excess (necessary) energy from the transmission system. Preliminary work explores price mechanisms to manage the microgrid and its interactions with the transmission system

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Modeling the natural gas supply chain for sustainable growth policy

Natural gas has been used globally as a transitional fuel for supporting a green-energy-supply strategy, which has been questioned for the intermittence and lack of reliability of renewables. This paper proposes a System Dynamics model for assessing alternative security of supply policy along the natural gas value chain. The model incorporates demand, transport, production and reserves of natural gas variables according to a systemic perspective. It also includes a module for evaluating the effect of natural gas price on the demand and supply levels, respectively. Alternative supply policies are evaluated under different scenarios. The chosen case-study focuses on the Colombian natural gas industry with the purpose of assessing how the impact of public policies affect supply and demand. Particularly, policies consider the allocation of resources along the natural gas supply chain, seeking to promote the development of infrastructure oriented to mitigate the risk of provision shortages

Framework for the optimization of operation and design of systems with different alternative water sources

Water security has become an increasing concern for many water system managers due to climate change and increased population. In order to improve the security of supply, alternative sources such as harvested stormwater, recycled wastewater and desalination are becoming more commonly used. This brings about the need for tools to analyze and optimize systems that use such sources, which are generally more complex than traditional water systems. Previous methodologies have been limited in their scope and cannot be applied to all types of water sources and systems. The framework presented in this paper has been developed for holistic analysis and optimization of water supply and distribution systems that use alternative water sources. It includes both design and operational decision variables, water and energy infrastructure, simulation of systems, analysis of constraints and objectives, as well as policies and regulations which may affect any of these factors. This framework will allow users to develop a comprehensive analysis and/or optimization of their water supply system, taking into account multiple types of water sources and consumers, the effect of their own design and operational decisions, and the impact of government policies and different energy supply options. Two case study systems illustrate the application of the framework; the first case study is a harvested stormwater system that is used to demonstrate the importance of simulation and analysis prior to optimization, the second utilizes four different water sources to increase security of supply and was optimized to reduce pump energy use.Lisa J. Blinco, Martin F. Lambert, Angus R. Simpson and Angela March

Reinforcement learning with probabilistic boolean network models of smart grid devices

The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics (using data analysis and asset condition to plan and perform activities). In this paper, we showcase the application of a complex-adaptive, self-organizing modeling method, and Probabilistic Boolean Networks (PBNs), as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. Different reward structures were created to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures.Peer ReviewedPostprint (published version

Impact of Demand-Response on the Efficiency and Prices in Real-Time Electricity Markets

International audienceWe study the effect of Demand-Response (DR) in dynamic real-time electricity markets. We use a two-stage market model that takes into account the dynamical aspects of gen-eration, demand, and DR. We study the real-time market prices in two scenarios: in the former, consumers anticipate or delay their flexible loads in reaction to market prices; in the latter, the flexible loads are controlled by an independent aggregator. For both scenarios, we show that, when users are price-takers, any competitive equilibrium is efficient: the players' selfish responses to prices coincide with a socially optimal policy. Moreover, the price process is the same in all scenarios. For the numerical evaluation of the properties of the equilibrium, we develop a solution technique based on the Alternating Direction Method of Multipliers (ADMM) and trajectorial forecasts. The forecasts are computed us-ing wind generation data from the UK. We challenge the assumption that all players have full information. If the as-sumption is verified, then, as expected, the social welfare increases with the amount of DR available, since DR relaxes the ramping constraints of generation. However, if the day-ahead market cannot observe how elastic loads are affected by DR, a large quantity of DR can be detrimental and leads to a decrease in the welfare. Furthermore, the DR operator has an incentive to under-dimension the quantity of avail-able DR. Finally, we compare DR with an actual energy storage system. We find that storage has a faster response-time and thus performs better when only a limited amount is installed. However, storage suffers from charge-discharge in-efficiency: with DR, prices do concentrate on marginal cost (for storage, they do not) and provide a better welfare

Adaptive Agents and Data Quality in Agent-Based Financial Markets

We present our Agent-Based Market Microstructure Simulation (ABMMS), an Agent-Based Financial Market (ABFM) that captures much of the complexity present in the US National Market System for equities (NMS). Agent-Based models are a natural choice for understanding financial markets. Financial markets feature a constrained action space that should simplify model creation, produce a wealth of data that should aid model validation, and a successful ABFM could strongly impact system design and policy development processes. Despite these advantages, ABFMs have largely remained an academic novelty. We hypothesize that two factors limit the usefulness of ABFMs. First, many ABFMs fail to capture relevant microstructure mechanisms, leading to differences in the mechanics of trading. Second, the simple agents that commonly populate ABFMs do not display the breadth of behaviors observed in human traders or the trading systems that they create. We investigate these issues through the development of ABMMS, which features a fragmented market structure, communication infrastructure with propagation delays, realistic auction mechanisms, and more. As a baseline, we populate ABMMS with simple trading agents and investigate properties of the generated data. We then compare the baseline with experimental conditions that explore the impacts of market topology or meta-reinforcement learning agents. The combination of detailed market mechanisms and adaptive agents leads to models whose generated data more accurately reproduce stylized facts observed in actual markets. These improvements increase the utility of ABFMs as tools to inform design and policy decisions.Comment: 11 pages, 6 figures, and 1 table. Contains 12 pages of supplemental information with 1 figure and 22 table

Microgrids

Integration of renewable energy sources in the electrical power system is key for enabling the decarbonization of that system. The connection of renewable generation to the electrical system is being performed in a centralized form (large renewable power plants like wind or solar power plants connected at the transmission system) and in a decentralized manner (through the connection of dispersed generation connected at the distribution system). The connection of renewable generation at distribution levels, together with other generating sources as well as energy storage systems (the so-called DER, Distributed Energy Resources) close to consumption sites, is promoting the development of microgrids: DER installations that have the capability to operate grid connected and grid isolated. The uncertainty and variability of the renewable energy sources that integrate microgrids, as well as the need for coordination with other energy sources, pose challenges in the operation, protection, control, and planning of microgrids. The five selected papers published in this Special Issue propose solutions to address these challenges.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.1 - Per a 2030, garantir l’accés universal a serveis d’energia assequibles, confiables i modernsObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaPostprint (published version

The Autonomous Attack Aviation Problem

An autonomous unmanned combat aerial vehicle (AUCAV) performing an air-to-ground attack mission must make sequential targeting and routing decisions under uncertainty. We formulate a Markov decision process model of this autonomous attack aviation problem (A3P) and solve it using an approximate dynamic programming (ADP) approach. We develop an approximate policy iteration algorithm that implements a least squares temporal difference learning mechanism to solve the A3P. Basis functions are developed and tested for application within the ADP algorithm. The ADP policy is compared to a benchmark policy, the DROP policy, which is determined by repeatedly solving a deterministic orienteering problem as the system evolves. Designed computational experiments of eight problem instances are conducted to compare the two policies with respect to their quality of solution, computational efficiency, and robustness. The ADP policy is superior in 2 of 8 problem instances - those instances with less AUCAV fuel and a low target arrival rate - whereas the DROP policy is superior in 6 of 8 problem instances. The ADP policy outperforms the DROP policy with respect to computational efficiency in all problem instances

Machine Learning in Aerodynamic Shape Optimization

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems