Operation and Planning of Energy Hubs Under Uncertainty - a Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence-based methods

Abstract: Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI‐based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems

Operation and planning of energy hubs under uncertainty - A review of mathematical optimization approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.Web of Science117228720

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

An insight into the integration of distributed energy resources and energy storage systems with smart distribution networks using demand-side management

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints

Quarterly GDP forecast based on coupled economic and energy feature WA-LSTM model

Existing macroeconomic forecasting methods primarily focus on the characteristics of economic data, but they overlook the energy-related features concealed behind these economic characteristics, which may lead to inaccurate GDP predictions. Therefore, this paper meticulously analyzes the relationship between energy big data and economic data indicators, explores the coupling feature mining of energy big data and economic data, and constructs features coupling economic and energy data. Targeting the nonlinear variation coupling features in China’s quarterly GDP data and using the long short-term memory (LSTM) neural network model based on deep learning, we employ wavelet analysis technology (WA) to decompose selected macroeconomic variables and construct a prediction model combining LSTM and WA, which is further compared with multiple benchmark models. The research findings show that, in terms of quarterly GDP data prediction, the combined deep learning model and wavelet analysis significantly outperform other methods. When processing structurally complex, nonlinear, and multi-variable data, the LSTM and WA combined prediction model demonstrate better generalization capabilities, with its prediction accuracy generally surpassing other benchmark models

Capturing Transmission and Distribution Connected Wind Energy Variability

Although renewable energy provides a viable solution to address ongoing challenges of the economy and the environment in modern power systems, the variable generation of this technology results in major technical challenges for system operators. This issue is becoming more severe as the penetration of renewable generation is increasing. This dissertation addresses the variability challenge of renewable energy resources in transmission and distribution levels of modern power systems. For transmission level, this dissertation focuses on wind generation fluctuation. Three methods of reducing wind generation fluctuation are investigated from an economic perspective, including (a) dumping the wind generation, (b) using battery energy storage system (BESS) to capture excess wind generation, and (c) a hybrid method combining these two approaches. The economic viability of the hybrid method is investigated via a developed linear programming model with the objective of profit maximization, which in extreme cases will converge to one of the other methods. This dissertation further proposes a BESS planning model to minimize wind generation curtailment and accordingly maximize the deployment of this viable technology. For distribution level, this dissertation investigates the issue of microgrids net load variability stemmed from renewable generation. This is accomplished by investigating and comparing two options to control the microgrid net load variability resulted from high penetration of renewable generation. The proposed options include (a) Local management, which limits the microgrid net load variability in the distribution level by enforcing a cap constraint, and (b) Central management, which recommends on building a new fast response generation unit to limit aggregated microgrid net load variability in the distribution level. Moreover, the aggregated microgrid net load variability is studied in this dissertation by considering the distribution system operator (DSO). DSO would calculate the microgrids net load in day-ahead basis by receiving the aggregated demand bid curves. Accordingly, two models are proposed considering the DSO role in managing the grid operation and market clearing. The first one is security-constrained distribution system operation model which maximizes the system social welfare. The system security consists of distribution line outage as well as microgrid islanding. None of these two security events are in the control of the DSO, so associated uncertainties are considered in the problem modeling. The second one aims at reconfiguring the distribution grid, i.e., a grid topology control, using the smart switches in order to maximize the system social welfare and support grid reliability. The conducted numerical simulations demonstrate the effectiveness and the merits of the proposed models in identifying viable and economic options in capturing renewable generation variability