Assessing storage and substitution as power flexibility enablers in industrial processes

Guest Editorial: Situational awareness of integrated energy systems

The high penetration of inverter-fed renewable energy sources (RESs) in modern energy systems has led to a reduction in the system’s inertial response. This reduction in the rotational inertial response is associated with synchronous generation and might result in a deteriorated frequency response following a power disturbance. This paper investigates the frequency stability of the Kingdom of Saudi Arabia’s (KSA) grid. It includes a description of the changing energy landscape of the KSA’s electricity grid and an investigation of the impact of high penetration levels of inverter-fed RESs on the dynamic behavior of the KSA grid. The impact of RESs has been studied through a simulation of case studies of the future KSA power system using the MATLAB/Simulink simulation software. The frequency stability of the KSA’s power system has been evaluated with various RES levels under peak and base load conditions. The simulation results show that the high penetration levels of RESs dramatically affect the system’s frequency response, especially under off-peak conditions. In addition, the significance of battery energy storage systems (BESSs) for compensating the reduction in the system inertial response has been addressed. The results show the effectiveness of aggregated BESSs for enhancing the system frequency control of the KSA grid.

Operational flexibility is an important property of electric power systems. The term flexibility is widely used in the context of power systems although at times without a proper definition. The role of operational flexibility for the transition of existing power systems, many of them based on fossil fuels, towards power systems effectively accommodating high shares of variable Renewable Energy Sources (RES) has been widely recognized. Availability of sufficient operational flexibility is a necessary precondition for the grid integration of large shares of power in-feed from variable RES, for example wind power and photovoltaics (PV). The paper analyzes the role of operational flexibility in power systems. Necessary flexibility metrics for categorizing different types of operational flexibility are discussed. A new methodology for assessing the technically available operational flexibility is presented. Qualitative insights are derived, notably regarding the limits of RES integration for a given power system with its specific flexibility properties. An extensive simulation study is performed, assessing the role that operational flexibility has for the mitigation of challenges, namely curtailment, arising from high shares of variable RES in-feed.

Chapter 9 - Integrated Planning of Energy and Water Supply in Islands

Extreme prices in electricity balancing markets from an approach of statistical physics

In the recent days, energy mix performed by using the Renewable Energy Sources (RES) is gaining wide popularity. The control of predictability and generation takes a progressive loss as RES is penetrating increasingly in the energy mixes due to the non dispatchable nature of the energy produced from these sources. For ensuring and maintaining stable operation, the power system flexibility is increased when higher penetration levels are attained by RES. A Decision Support System (DSS) is used for energy conversion and storage systems which can be managed by an Information and Communication Technology (ICT) tool. The H2020 PLAnning and operational tools are used for optimizing energy flows and synergies between energy NETworks (PLANET) along with DSS for controlling the technology wherein power is converted into either heat or gas (P2X) by evaluation, management and dispatch. Further analysis is done in terms of energy evaluation and economic benefits with respect to P2X technology and its flexibility

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems.

Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources

The analysis of existing mathematical models for power systems with large shares of electricity production by NPP and renewable energy sources generation was carried out. The low level of adequacy is shown for power system models used in the problems of long-term development planning for generating plants with nuclear fuel and renewable energy sources. In order to plan the development of power systems at the current stage of technological changes in the electric power industry, new models of load modes for NPP units and generating equipment with renewable energy sources are proposed. Dispatching models with a cyclic forecast period were used to ensure the adequacy unit commitment models for NPP units and energy storage systems. Representative models of solar and wind power plants are presented, which, together with cluster models of NPP units and energy storage systems, solve the issue on the adequacy of power system modeling in current conditions of their development. The results of computational experiments with the proposed models for generating equipment of the flexible grid are presented. The adequacy of cluster mathematical models of NPP units and energy storage systems, as well as representative models of solar and wind power plants generating equipment is experimentally confirmed.

Modern power systems are pushed to operate close to their secure limits due to increased system complexity and high penetration of renewable energy sources (RES). Conventional stability assessment (SA) and control methods face significant adaptiveness and robustness issues under highly uncertain and variable modern power systems. In recent years, with advanced real-time system monitoring and communication techniques like phasor measurement unit (PMU) devices, huge volumes of network-wide time-synchronized measurements can be collected and utilized. Under this new reality, data-driven methods have been identified as powerful tools for online and real-time stability assessment and control. This Ph.D. research aims to develop data-analytic methodologies online and real-time assess the system stability status and provide intelligent control actions when the power system is subject to a severe contingency. Specifically, this research covers a series of new methods in pre-fault SA, post-fault real-time SA and power system emergency control. Special efforts are devoted to enhancing the accuracy and robustness of data-driven methods under some extreme scenarios and practical problems, and a confidence-aware data-driven control scheme to manage the potential prediction error better. In data-driven SA, the point and consecutive data missing at both offline and online stages are studied, and two missing data-robust methods are developed, respectively for pre-fault SA and post-fault SA. For the pre-fault method, to enhance the robustness of SA against incomplete PMU measurements, a hybrid ensemble learning-based online SA method is proposed with feature-validation-based missing data estimators. Considering the possibility that some features may have a low correlation with other measurements, a feature validation process detects the potentially inaccurately estimated features to ensure the model performance and data restoration quality. Further, a novel spatial-temporal recurrent imputation network, SRIN, is proposed for time-series data missing at both sensor and PMU levels. The proposed SRIN method jointly learns the imputation for missing values and prediction output through an integrated multi-task recurrent network model, where the imputed values and the SA output can be simultaneously and effectively updated during backpropagation. In this way, the proposed method can be effectively trained by incompletely recorded datasets, which is immensely valuable for practical applications without high-quality historical data. Moreover, by learning shared representations or features across tasks, multi-task models can better exploit the information present in the data and improve the overall performance of both tasks. In addition, considering the time-varying and location-varying missing data patterns, the imputations of SRIN are derived by an intelligent combination of history-based and feature-based estimations through a trainable weighting component. Thereby, the proposed method is highly adaptive to various data missing scenarios, including both spatial and temporal consecutive missing patterns. In emergency control, data-driven methods for event-based load shedding (ELS) and response-based load shedding (RLS) are developed. Specifically, a hierarchical data-driven method is proposed for real-time prediction of ELS to mitigate fault-induced delayed voltage recovery (FIDVR). First, a novel adaptive trajectory sensitivity analysis is developed to optimize the ELS solutions offline and construct the ELS database. Then, by hierarchically modelling the ELS problem as a multi-output classification subproblem and a regression subproblem, the critical load buses mainly responsible for the delayed voltage recovery can be identified, and the over-shedding buses can be effectively reduced. Moreover, based on an improved weighted kernel extreme learning machine (WKELM) method, the direct mapping between system pre-fault operating conditions and corresponding control variables is constructed. Meanwhile, the established weighted learning scheme can extract the decision-making knowledge from the highly sparse and imbalanced dataset and emphasize on the rare yet critical cases. Therefore, the risk of insufficient load shedding is reduced, and better ELS effectiveness can be achieved. Subsequently, a probabilistic confidence-aware method for real-time RLS is developed to manage the potential prediction error better. The method is based on a two-stage process where a scalable Gaussian process (SGP) method determines the average load shedding amount at the first stage. The upper bound of the prediction interval is considered supplementary in the second stage. In parallel, if an RLS case is identified as unconfident, the conservative rule-based load shedding is taken as a backup plan rather than adopting LS estimations that cannot be trusted. Guided by the probabilistic information, the potential prediction error can be fully considered, and the confidence information can be incorporated into the control process, thereby more accurate and reliable load shedding actions can be taken. Leveraging confident predictions-based 2-stage RLS process, the excessive load loss can be reduced while ensuring high control effectiveness. All the proposed methods have been well verified on simulation experiments over standard power system models.

Communication routing of cyber‐physical power systems (CPPS) with the high penetration of renewable energy sources (RES) plays an important role in the resilience enhancement against disasters, natural and man‐made alike. Therefore, a trilevel optimization model is proposed for the CPPS resilience enhancement with the RES against extreme events. The upper‐level model identifies optimal hardening lines of both the transmission and communication networks with consideration of the communication routing constraints. The middle‐level model identifies the attacked lines to maximize load shedding of the power system. The effects of RES uncertainty and communication routings on resilience enhancement are analysed. The lower‐level model attains an optimal allocation strategy of power generation to minimize load shedding. The model is solved by the column‐and‐constraint generation algorithm. Case studies are conducted on the IEEE 14‐bus, RTS 24‐bus, and 118‐bus system. The results show that the proposed hardening strategy can effectively ensure the adaptiveness of the transmission network and communication routing to improve the resilience of CPPS to extreme events. Moreover, it is observed that the load loss and total investment cost are heavily affected without and with RES.

Extremely penetration of Renewable Energy Sources (RES) into modern power systems inflicts many challenges when it comes to achieve optimal performance out of overall system. One of the potential issues is that RES are electromagnetically de-couple from the rest of the system as they are interfaced through power electronics with the traditional grid, which gives rise to synchronization problems and improper control of “rate of change of frequencies (ROCOF)”. Moreover, variations in weather and operating conditions adversely effect their non-linearity performance, which causes their insufficient participation towards frequency regulation along with other conventional synchronous generators. Furthermore, uncertainty associated with RES because of their intermittent nature introduces different dynamic characteristics, which increases complexity of control systems and operation. Some other concerns are degraded system inertia, high fault current, low power quality, high uncertainties, and low fault ride through capability. As a solution to these RES integration problems, cutting edge technologies including different control strategies, inertia emulation techniques, optimization techniques, energy storage devices, and fault current limiters are being practically adopted. This paper is intended to provide a comprehensive overview of challenges faced by modern power systems because of the rapidly rising RES penetrations. Further, this literature presents some practical approaches and solutions to conquer these challenges. Overall, this paper is an intensive source of information about the operational strategies and challenges of modern smart power systems including various kinds of RES as their constituents.

The escalating grid-connected capacity of renewable energy sources, predominantly wind and photovoltaic (PV) power, along with its inherent volatility and anti-peaking attributes, exacerbates the peaking demands on the power system. Consequently, this trend necessitates enhanced flexibility in power system peaking capabilities. In response to this challenge, this paper introduces an optimal scheduling methodology grounded in a two-stage stochastic model tailored for power systems, which incorporates thermal-storage peaking pricing. Initially, a hierarchical decision-making framework, employing the group decision hierarchy analysis method, is devised to formulate a peaking pricing strategy for thermal power and energy storage. Subsequently, acknowledging the variability of wind power, multiple scenarios are generated and consolidated through the application of k-means clustering. A two-stage stochastic optimization approach is then utilized for day-ahead pre-dispatch of thermal power and storage units, and intraday dispatch adjustments are made to accommodate the uncertainties associated with renewable energy sources by leveraging flexible resources, thereby ensuring power balance, and maximizing wind power integration. The efficacy of the proposed approach is validated through simulations conducted on the enhanced IEEE39-bus system. The numerical results show that the flexibility of the system can be effectively improved, and the wind power and photovoltaic accommodation rate is increased by 98.11 and 99.42%, respectively.

Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response

With increasing penetration of renewable energy sources and electric vehicles (EVs) in power grids, developing appropriate models for power system planning is of great importance. This article proposes a novel gray wolf optimization (GWO) algorithm-based transmission expansion planning (TEP) method considering renewable energy sources and EVs and evaluates the performance of alternating current power flow (ACPF) and direct current power flow (DCPF) models in the TEP problem in terms of accuracy, run-time, and objective functions. The uncertainty associated with renewable energy sources, EVs, and loads are explicitly modeled in this study using probability density functions (pdfs) and the Copula approach. The proposed GWO-based TEP method is validated by case studies using the IEEE 24-bus reliability test system (RTS) and IEEE 118-bus test system. The ACPF model leads to fewer transmission lines and lower costs than the DCPF model in most scenarios.