Security Assessment and Optimization of Energy Supply

Oeconomia Copernicana, 2015

The question of energy supply continuity is essential from the perspective of the functioning of society and the economy today. The study describes modern methods of forecasting emergency situations using Artificial Intelligence (AI) tools, especially neural networks. It examines the structure of a properly functioning model in the areas of input data selection, network topology and learning algorithms, analyzes the functioning of an energy market built on the basis of a reserve market, and discusses the possibilities of economic optimization of such a model, including the question of safety.

ArXiv, 2021

Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy which serve as critical infrastructures for most societal operations. However, due to the requirements of reliability, accountability, and explainability, it is risky to directly apply AI-based methods to power systems because society cannot afford cascading failures and large-scale blackouts, which easily cost billions of dollars. To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model. The goal is to provide a high level of confidence to energy utility users. For illustration purposes, the paper uses power system event forecasting (PEF) as an exa...

Technology audit and production reserves, 2023

The object of research is the energy supply company and the processes of generation and supply of electric energy. The paper examines the problems of building mathematical models for forecasting the operation of a critical infrastructure object in the conditions of a changing security environment, characterized by unpredictability, the presence of uncertainties of various types, the appearance of new threats, their combinations, changes in the form, duration, nature of their influence. In the work, the main attention is paid to the study of the functioning of critical infrastructure in the field of energy supply. Based on the study of the functioning of the energy company system, methods of dealing with uncertainties at the stage of data preparation for analysis and during the preliminary construction of models are presented, in particular, statistical and probabilistic approaches, modeling of the studied processes, alternative methods of estimating model parameters, etc. The complexity of preparing the input data set is related to the fact that it is necessary to ensure the representativeness and variability of the data sets, given that a significant number of factors must be included in the model according to the requirements of regulatory documents, which can lead to multicollinearity of the input variables. The paper proposes an analytical toolkit based on the use of mathematical models and their combinations, intended for use in specialized decision support systems. In the course of the research, a number of numerical experiments were conducted, in which the proposed methodology was worked out on the materials of the enterprise-the object of the critical infrastructure of the energy sector. SAS Energy Forecasting software was used to build the models. The best forecasting results are obtained using generalized linear models (GLM), in particular the GLM model in the form of ARIMAX (a moving average autoregressive model that includes an integrated trend component and external regressors). The proposals presented in the work will allow to increase the efficiency of the functioning of the energy sector, including the determination of the goals, tasks and benchmarks of its operation in regular mode for certain periods of time, as well as in the field of development of universal and special mechanisms for ensuring stability in the mode of response to threats and critical situations.

Southern African Institute of Industrial Engineering 2013, 2013

Energy supply is the backbone of almost all economic activities powering critical systems and infrastructures required for the functioning of our modern economies and societies. Regardless of where and how they are produced, energy must be delivered to the points of consumption and any disruptions in the chain can rapidly degenerate into a national crisis. However, sustaining an undisrupted production and supply of this vital resource has been identified as a major challenge globally with issues like terrorism, spiralling and unstable energy prices, technology uncertainties and natural disasters posing serious threats to the supply chain. This study develops a risk management framework for the energy supply chain using artificial neural networks as the transformation and simulation engine. To get a reliable estimate of the health of the critical path in the supply chain, a few genetic algorithms were tested and the one with best performance chosen for this framework after a comparative analysis.

IEEE Intelligent Systems, 2000

Load forecasting has always been an essential task for the electric power utilities all around the world, in which it may assist to an effective operational planning and security assessment of a power system. This is important to ensure that the electric

Annals of Operations Research

The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.

IEEE Access

Boosting the complexity of the electricity network, penetration of renewable resources, and modernization of power systems has resulted in an increase in the complexity of the power systems security assessment (PSSA). In this context, to decrease the vulnerability of the systems to multiple instability threats and security issues while ensuring the safe operation of the power systems, providing effective online security assessment methods capable of monitoring the systems' security under varying conditions is vital. However, although the traditional methods have demonstrated efficient PSSA performance, intelligent data-driven approaches have effectively overcome the traditional approaches by delivering impressive and rapid PSSA performance. Artificial intelligence (AI)-based techniques are required to guarantee the efficient, optimal, and safe security assessment. The usage of AI is emphasized due to its computational speed for online performance and its flexibility for providing corrective actions for insecure operating conditions to achieve a seamless transition in power systems. In this review, various available data-driven methods in power system security are comprehensively reviewed into two primary classifications: static and dynamic security assessment. The evaluated study aims to highlight the merits and demerits of developed techniques as well as their limitations to provide decision-making assistant for future investigations. INDEX TERMS Power systems security assessment, data-driven, artificial intelligence, machine learning. ABBREVIATIONS AANN Artificial adaptive neural network. AEP American electric power. ANFIS Adaptive neuro-fuzzy inference system. ANN Artificial neural network. CART Classification and regression trees. CNN Convolutional neural network. CVM Core vector machine. DA Data acquisition. DE Differential evolution. DG Distributed generation. DL Deep learning. The associate editor coordinating the review of this manuscript and approving it for publication was Yu-Huei Cheng. DSA Dynamic security assessment. DT Decision tree. EML Extreme machine learning. FIS Fuzzy inference system. GA Genetic algorithm. GAN Generative adversarial network. IoT Internet of things. IRF Iterated random forest. MFNN Multi-layer feed-forward neural network. MLP Multilayer perceptron. PD Pattern discovery. PMI Partial mutual information. PMU Phasor measurement unit. PNN Probabilistic neural networks

2013 IEEE Power & Energy Society General Meeting, 2013

Recent blackouts in the USA, Europe and Russian Federation have clearly demonstrated that secure operation of large interconnected power systems cannot be achieved without full understanding of the system behavior during abnormal and emergency conditions. Current practice of managing separate parts of the system without knowledge of the 'full picture' will lead to even greater blackouts. This paper proposes a novel approach to the system monitoring and control with the goal of identification of potential voltage instability problems before they lead to major blackouts. The proposed approach is based on detecting alarm states using self-organized Kohonen neural networks, and activating a multi-agent control system to take necessary preventive actions. The Kohonen network is trained off-line and then applied on-line to predict possible emergencies. The intelligent system was realized in STATISTICA 8.0 and tested on the modified 42-bus IEEE power system. Results are presented and discussed.

Engineering Applications of Artificial Intelligence, 2008

In this paper, next-day hourly forecasts are calculated for the energy price in the electricity production market of Spain. The methodology used to achieve these forecasts is based on artificial neural networks, which have been used successfully in recent years in many forecasting applications. The days to be forecast include working days as well as weekends and holidays, due to the fact that energy price has different behaviours depending on the kind of day to be forecast. Besides, energy price time series are usually composed of too many data, which could be a problem if we are looking for a short period of time to reach an adequate forecast. In this paper, a training method for artificial neural nets is proposed, which is based on making a previous selection for the multilayer perceptron (MLP) training samples, using an ART-type neural network. The MLP is then trained and finally used to calculate forecasts. These forecasts are compared to those obtained from the well-known Box-Jenkins ARIMA forecasting method. Results show that neural nets perform better than ARIMA models, especially for weekends and holidays. Both methodologies calculate more accurate forecasts-in terms of mean absolute percentage error-for working days that for weekends and holidays. Agents involved in the electricity production market, who may need fast forecasts for the price of electricity, would benefit from the results of this study.