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Congestion in transmission lines will not be tolerable except for a short time as it may cause power outages and losses. In this paper, through multistage congestion management (CM), a conceptual framework for transmission CM is presented. At first, by the contingency analysis, uncertainties are prioritized based on their impacts on the congestion. Those with severe impacts (in congestion) are taken into account in the day ahead, while those with minor impacts are considered in the real time. It is because of the fact that all uncertainties do not happen in reality and considering all of them in the day ahead imposes high additional costs. In other words, the minor effect uncertainties are neglected in day ahead and are postponed to real time. Hence, even if they happen, they will be overcome by remedial measures with no significant concern. Then at each time interval (for both day ahead and real time), using the sensitivity analysis a congestion control zone is formed for each congested line. Finally, through the intelligent implementation of the nonlinear demand response programs (DRPs) and an alternative load shedding model, the congestion is alleviated. Results prove the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper describes a novel algorithm for line overload alleviation with minimum load curtailment in the presence of wind turbine doubly fed induction generator (WTDFIG). Emergency conditions may arise due to predicted load in the next interval or outage of a transmission line. Selection of load buses for load shedding has been identified by sensitivities of severity index. Bounds on bus voltages have been considered in addition to line flow limits. Crow search algorithm (CSA) modified with Lampnien's criterion to account inequality constraints has been employed to obtain optimal solution of the formulated problem. A significant increase in electric vehicles (EVs) is observed each year. This, in turn, causes an increase in EV charging substations to meet the load demand. In view of this, role of renewable energy sources (RESs) has increased to maintain the continuity of supply to EVs charging substations. The developed algorithm has been implemented on 25-bus and 118-bus standard IEEE test systems. Results have been compared with those obtained using SFLA (shuffled frog leaping algorithm) and CAPSO algorithm based on statistical inferences. [ABSTRACT FROM AUTHOR]

Increased stress in traditional power systems results in blackouts due to voltage instability attributed to a mismatch between available capacity and load demand, especially in distribution networks. Service restoration schemes are designed to return power supply to the affected parts of the networks. The availability of insufficient supply is a complex problem that requires operational experience or an automatic system. The stochastic nature of load demand significantly impacts service restoration as it results in increased restored demand in case a fault occurs during off-peak hours and helps reduce overload if the fault occurs during peak hours. The study adopts an experimental design methodology to develop the Reinforcement Learning-based service restoration algorithm considering the stochastic nature of load demand. Three reinforcement learning models were used to develop the optimal load shedding model, including Actor-Critic (A2C), a Deep Q Network (DQN) and Proximal Policy Optimization (PPO2), and compared to maximize restored customers, satisfaction of operational constraints, and balancing of power supply and demand. The Particle Swarm Optimization (PSO) algorithm, a metaheuristic algorithm, was also implemented to compare with the proposed approach. The proposed solution has been tested using data from a real electrical secondary distribution network. The proposed solution considered the stochastic nature of load demand, resulting in more restored customers. The computation time during restoration has been improved by 69.8% compared to the metaheuristic approach. [ABSTRACT FROM AUTHOR]

The idea of renewable resource integration influences the demand-supply balance significantly. The effort taken to diminish the deviation in demand-supply balance is greatly influenced by demand response (DR) strategy. In this analysis, the concept of DR is incorporated via the approach of load management. The optimal scheduling of the load considering the available energy helps to avoid unessential load shedding. The DR strategy of load shifting is suggested at different levels in accordance with the nature of load and real-time price. The profit of the utility is enhanced by providing the customer the flexibility to schedule their load. The curtailment of load can be avoided by increased customer participation in the suggested DR programs. The operating system is designed to be both efficient and effective by utilizing the real-time tariff profile of electricity to plan the generation of renewable energy, energy storage systems that include electric vehicles, and the utilization of grid power. The scalability and generalization of the proposed DR approach make it suitable for large-scale power systems that integrate renewable energy sources. The validation of the proposed method using reliability analysis ensures the effectiveness of the formulated DR. The proposed method is tested under two different scenarios and topologies on the IEEE-33 bus system and the results accomplished are promising. [ABSTRACT FROM AUTHOR]

Utility Grid outages may affect the industrial power system load-generation balance and cause rapid frequency decays particularly due to low inertia and small spinning reserve. Frequency stability is an essential perspective for the invulnerable operation of islanded industrial power systems with in-plant generation. Multi-machine system frequency response (MMSFR) model with the dynamic of governors, overall inertia of the system (generators and loads), and load damping is considered to find the approximate value of load shedding (LS) amount. The frequency variation of the islanded system is evaluated for various operating scenarios with a calculated approximate LS amount to determine the optimum amount of LS. In this paper, an artificial neural network (ANN)-based optimum and adaptive LS technique have been presented. The total in-plant generation, spinning reserve, total power import, total demand, and frequency decay rate have been selected as the input neurons of the ANN. The regrouping particle swarm optimization (RegPSO) algorithm has been adopted to obtain more accurate and faster training of the ANN. The oil refinery power distribution system with in-plant generation is considered to evaluate the performance of the proposed RegPSO-ANN load shedding algorithm. The performance of the proposed scheme is evaluated in comparison with the under-frequency relay-based conventional load shedding scheme and Levenberg–Marquardt back-propagation ANN-based LS scheme. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources (RESs) in modern power systems poses two conflicting issues. First one is the reduction in the operation costs resulted from RESs utilization instead of the expensive fossil fuel thermal generating units. However, the RESs are characterized by its uncertain and intermittent behavior that their output power is not dispatchable and not known exactly due to forecast errors. Therefore, reserves are scheduled in the day-ahead market to meet the uncertain supply from RESs which add some costs to the system. The decision maker should be aware of those two conflicting objectives in order to operate the system in the optimal way with minimum operation costs. The problem of the market clearing is formulated as Mixed Integer Linear Programming (MILP) problem using GAMS software. It is considered a two stage stochastic programming with the objective of minimizing the expected total operation energy and reserve costs while satisfying the various operational constraints. The results show the effectiveness of the RESs integration in different cases with the consideration of load shedding, RESs curtailment and transmission congestion. As shown from the results, when the network is congested, the operation costs are increased due to the load shedding and RESs curtailment events occur. Moreover, scheduled reserves are increased to face the uncertainty of RESs. As the RESs power penetration increases with no network congestion, a decreasing trend in the operation cost can be seen. However, this trend is less marked as the uncertainty of RESs generation increases. For the network congested case, the reduction rate of the operation cost is decreased for increasing RESs power penetration levels. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive under frequency load shedding (UFLS) model that can be implemented in a multi‐area power system with real network characteristics. Conventional single‐area UFLS models operate on the basis of an equivalent center‐of‐inertia (COI) model, which ignores the local dynamics of system frequency response (SFR) and the impacts of load shedding location. Unlike the single‐SFR model, that is commonly utilized in previous works, the suggested multi‐area or multi‐SFR UFLS plan of this research has the distinct benefit of taking into account the dynamics of power transfer across different electric areas. The proposed multi‐area UFLS design incorporates a flywheel energy storage system (FESS) to support the inertial system frequency response and alleviate more than 30% load shedding while improving the frequency nadir by 25%. In order to investigate the performance of the proposed method under high penetrations of inertia‐free renewables, the inertia of the power network is reduced by around 30%; therefore the proposed UFLS scheme is assessed under a low inertia scenario. The proposed multi‐stage UFLS scheme is formulated as a mixed‐integer linear programming problem, and the optimal settings, including frequency set‐points and load shedding, are then optimized. The efficiency of the proposed model is verified using the IEEE‐118 Bus dynamic test system. [ABSTRACT FROM AUTHOR]

The article focuses on the potential benefits of switching the fuel for the Ankerlig peaking plant in South Africa from diesel to liquefied petroleum gas (LPG) to reduce costs, improve greenhouse gas emissions, and address the country's ongoing issue of load shedding. Topics discussed include the significance of the proposed fuel change, the current challenges with load shedding in South Africa, the economic and technical feasibility of using LPG as an alternative to diesel.

The article focuses on the relationship between load shedding and mental health in South Africa. It critically examines claims that load shedding negatively affects mental health and highlights the lack of empirical evidence to support these claims, methodological challenges in collecting relevant data, and the conflation of psychological distress with psychopathology.

The article focuses on the hidden costs of load shedding in South Africa. Topics include the economic impact of load shedding on short-term and long-term growth, the strain on social cohesion, and the quantification of costs related to discretionary electricity use, such as water heating, electric vehicles, and poorly insulated dwellings.

The article focuses on the impact of increasing residential battery backup systems on load shedding in South Africa. Topics discussed include the factors leading to rolling blackouts, the installation of battery backup systems without solar generation, and how these installations affect the grid's stability during load shedding, highlighting the need to restrict the charging rate of batteries to protect load shedding as a grid-balancing tool.

The article focuses on the ongoing issue of load shedding in South Africa, with the primary significance being its impact on income inequality. It discusses how low-income households are disproportionately affected by power interruptions, leading to economic disparities and challenges in accessing essential services. It analysis also explores the potential exacerbation of income inequalities by policies aimed at addressing load shedding.

Active islanding detection techniques majorly affect power quality due to injected harmonic signals, whereas passive methods have a large non-detection zone (NDZ). This article presents a new method based on the resultant sequential impedance component (RSIC) as a new approach to island detection with zero NDZs. The abrupt variable in the conventional impedance approach was replaced by the RSIC of the inverter in this method. When the measured value exceeds the threshold range, islanding is detected by monitoring the variations in the RSIC at the point of common coupling (PCC). For proper power utilization in the identified islands, a priority-based load-shedding strategy is also recommended and implemented in this article. Its efficacy was verified in a wide range of real-world settings. It offers superior stability in various non-islanding (NIS) scenarios to prevent accidental tripping. The proposed method advantages include a cheap cost, the simplicity of implementation, independence from the number and type of distributed generation (DG) units connected, and no power quality effects. Compared to other methods reported in the literature, the obtained detection times illustrate that the proposed method is superior. [ABSTRACT FROM AUTHOR]

The rapidly drop of frequency in the microgrid (MG) system when it operates under island operation has caused frequency stability problems. In case, generators and renewable energy sources cannot supply enough power to restore the frequency, the load shedding will be proceeded. This paper proposes the load shedding method based on the coordinate multiple load shedding factors when the MG system operates in island mode. This proposed method carries out an approach through two main steps. Firstly, the optimal amount of load shedding power considering renewable energy sources and the ability to adjust the primary and secondary frequencies of the generator is calculated to restore the frequency to the allowed range. Next, distributing the optimal load shedding power for each bus is based on the combination of different criteria to meet the economic and technical goals. On the technical side, this paper also considers two criteria about the voltage distance, and the voltage sensitivity index. These two criteria contribute to reducing voltage drop and improving voltage stability when the microgrid operates in island mode. From the economic perspective, it considers the load importance factor, thereby minimizing the economic losses caused by load shedding. The AHP algorithm is applied in this paper to determine the importance of loads and the importance of the different criteria when they are combined. The effectiveness of the proposed method is demonstrated through simulation of IEEE 16-bus microgrid with 6-source. [ABSTRACT FROM AUTHOR]

Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system. [ABSTRACT FROM AUTHOR]

At complex industrial sites, the high number of large consumers that make the technological process chain requires direct supply from the main high-voltage grid. Often, for operational flexibility and redundancy, the main external supply is complemented with small local generation units. When a contingency occurs in the grid and the main supply is cut off, the local generators are used to keep in operation the critical consumers until the safe shutdown of the entire process can be achieved. In these scenarios, in order to keep the balance between local generation and consumption, the classic approach is to use under-frequency load-shedding schemes. This paper proposes a new load-shedding algorithm that uses particle swarm optimization and forecasted load data to provide a low-cost alternative to under-frequency methods. The algorithm is built using the requirements and input data provided by a real industrial site from Romania. The results show that local generation and critical consumption can be kept in stable operation for the time interval required for the safe shutdown of the running processes. [ABSTRACT FROM AUTHOR]

The fault‐induced delayed voltage recovery (FIDVR) and short‐term voltage instability (STVI) phenomena appear in networks with high penetration of induction motor loads because the increase in requested reactive powers of these loads prevents the voltages from quickly returning to their pre‐fault levels. Load shedding (LS) is one of the ways to deal with FIDVR and STVI and reduce the imbalance between the generation and demand of reactive power. Under‐voltage (UV) relays that disconnect loads during voltage drop cannot effectively deal with this phenomenon because of their inability to detect the effective loads on the reduction of FIDVR and STVI severities. Therefore, the operation of UV relays during these phenomena causes unnecessary load disconnection, and the interference of their operations with FIDVR and STVI must be avoided. This paper presents two wide‐area approaches based on network and loads parameters, the first of which deals with the most critical FIDVR, and the second tries to simultaneously handle critical FIDVRs of all buses. Also, bus prioritization for LS during STVI has been addressed. Simulation results revealed the better performance of the proposed approaches than the previous ones in terms of the amount of LS and the number of selected buses for LS. [ABSTRACT FROM AUTHOR]

Background: The generation of clean and affordable energy by 2030 is a challenging task, necessitating the integration of renewable energy sources to reduce greenhouse gas emissions associated with coal, crude oil, and natural gas. This study examines the optimization and performance analysis of a hybrid microgrid for a university campus as a potential solution to achieve this goal. The primary objective is to decrease the cost of energy and reduce CO2 emissions on the campus using a hybrid approach. Results: The Howard college campus of the University of KwaZulu Natal (UKZN) was used as a case study, with meteorological data obtained from NASA and real hourly electrical load data for 2019 from the university smart meters. HOMER, an optimization software, was employed to model and simulate the case study. The results demonstrated significant savings of R15.7 million (approximately $ 820 000) in annual utility bills, a 51% reduction in CO2 emissions, a return on investment of 20%, and a payback period of 4 years. Conclusion: The study's findings suggest that universities can become self-sustaining during load shedding periods, as recently experienced in South Africa. The implementation of a hybrid microgrid system on a university campus offers considerable economic and environmental benefits, providing a potential blueprint for other large institutions seeking to achieve similar sustainability goals. [ABSTRACT FROM AUTHOR]

Ice disasters, such as ice storms, can cause serious damage to power systems. To understand ice disasters' influences on power systems, this paper introduces a resilience evaluation frame for transmission and distribution systems during ice disasters. First, we built a vulnerability model for transmission and distribution systems under ice disaster weather. Then, we established an optimal load power shedding model for transmission and distribution systems. After this, according to the vulnerability model and the optimal power load power shedding model, we generated the fault scenario set of a system in the influence of an ice disaster. According to the curve of system resilience, we propose two resilience evaluation indices of transmission and distribution systems under ice disaster weather. Finally, we verified the efficacy and rationalization of the established resilience evaluation framework with an example in which a transmission and distribution system is coupled with a six-bus transmission system and two distribution systems. This study highlights the necessity of resilience assessment of transmission and distribution systems during ice disasters. [ABSTRACT FROM AUTHOR]

Text classification is a crucial task in the text mining field, and it can be included in queries with user-defined functions(UDF). In many web applications, such as Twitter mining or Weibo real-time processing, when the amount of text data to be processed is enormous, there will be many overload phenomena. At the same time, when the system is overloaded, the delays in the query process can negatively affect the user experience in a streaming scenario. This paper focuses on the query with text classification on streaming data. We propose a novel method called Zebra with progressive pipelines to optimize the overload query situations. The core module of Zebra is the probabilistic filter which can reduce an incredible amount of text data based on semantic information of the query predicate. We train weak classifiers as filters using data with labels from brute-force pipelines. Next, we use a parameter search method to choose a suitable filter with the best settings and apply it to progressive pipelines. Experiments with several text workloads on real-world datasets show that Zebra can achieve higher accuracy stably while answering the query in time. [ABSTRACT FROM AUTHOR]

In order to solve the problem of excessive deformation of the snow-covered steel corrugated plate shed at the entrance of the tunnel under the action of deep snow load, taking a tunnel in Xinjiang as the project support, and proposed four load reduction optimization design schemes, namely, manufacturing icicles on both sides, manufacturing ice arches on the upper side, icicles and ice arches coupling, and icicles and ice arches expanded polystyrene (EPS) plate coupling. The finite element software ABAQUS was used to analyze the stress, deformation and feasibility of the four schemes. The results show that the stress and deformation of the steel shed are greatly reduced by the four load reduction optimal design schemes, and the structural stress and deformation meet the allowable values of the design code. Marston􀆳s theory of “equisettled surface” earth pressure can be applied to the piled snow body to increase the settlement difference between the inner and outer snow column and reduce the vertical pressure on the structure. The flexible material of EPS plate has a certain load reduction effect in the high snow condition, but it is not significant in the high fill soil. In the high snow condition, it is recommended to give priority to encrypt the snow body around the corrugated steel plate to achieve the load reduction effect. [ABSTRACT FROM AUTHOR]

Aiming at the influence of wind power and load uncertainty on the transient stability of a power system under low carbon mode, this paper first proposes a collaborative preventive and emergency control model of transient stability by distribution preserving graph representation learning (DPG). Second, the uncertainty set of wind power output and load demand is studied, and the mathematical form of the two-stage robust transient stability collaborative control model is proposed. Then, the latest artificial intelligence technology is embedded into the global optimization algorithm of the model so as to further improve the solving efficiency of the algorithm. Finally, based on the developed improved two-stage robust optimization framework, an effective collaborative control method for transient stability is developed. The transient stability prediction and control system developed in this project is not only conducive to large-scale wind power grid connection but also expected to make academic contributions to development of power system transient stability and practical simulation verification. [ABSTRACT FROM AUTHOR]

Minimizing the emissions produced during the processing of biofuel, one aim is to reduce or completely replace the amount of the required fossil fuels used for internal process energy. For the transition of process energy from fossil to renewable energy sources, such as solar and wind, the energy demand of biomass processing must be adjustable to the fluctuating electricity supply. The flexible adjustment of a system's power demand to follow the current power generation is commonly referred to as demand side management (DSM). This contribution shows the results of a study on the implementation of DSM in biofuel biorefineries. By identifying reference concepts that could represent biofuel production plants, the specific mass and energy consumption for the individual process steps in these reference concepts was analyzed through a literature study. The annual throughput and energy consumption of process steps in biofuel production could then be calculated, enabling the identification of the most energy‐consuming process steps. Subsequently, possible flexible operating load ranges of the respective process steps in biofuel production were identified. These findings allowed an assessment of the potential for different process units of biorefinery systems concerning the quantitative adaptability of the electricity load – the theoretical DSM potential. An approximate theoretical DSM potential of 146 MW has been identified for biofuel production in Germany. This cumulated theoretical DSM potential in biofuel production was compared to that of other industrial processes, demonstrating the magnitude and importance of the implementation of DSM in biofuel production. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

This study focuses on modelling and controlling hybrid Photovoltaic (PV) and wind energy systems for Electric Vehicle (EV) battery charging stations. A load shedding mechanism based on Deep Neural Networks (DNN) has been developed. The integration of this mechanism with the grid through a voltage source converter (VSC) has been examined. The study emphasizes the integration of a DNN-based controller and maximum power point tracking (MPPT) algorithm. The proposed system aims to reduce energy costs and optimize the load on the grid by increasing the use of renewable energy sources. This innovative approach has been developed to address load balancing and stability issues in existing energy management systems. The primary goal of the research is to ensure more efficient use of renewable energy sources and to optimize energy demand management. Simulations conducted under various operating conditions have evaluated the ability of the proposed DNN controller to manage loads dynamically according to three priority levels. According to the results, the proposed system improved load shedding by 25% and increased overall system stability by 15%. The performance rate of the DNN controller was measured at 98%. This indicates that the system operates with high accuracy. In conclusion, the proposed system validates its effectiveness in managing load shedding, maintaining system stability in energy management and optimizing renewable energy sources. [ABSTRACT FROM AUTHOR]

The article focuses on the mental health impact of ongoing energy crises, particularly load shedding, in South Africa. It highlights the psychological distress experienced by the population, which is often underestimated due to the emphasis on their resilience. It mentions the calls for comprehensive academic research and increased attention from mental health professionals to address the consequences of load shedding on the well-being of South Africans.

RES-based microgrids are often threatened by critical conditions due to variations in solar radiation in PV plants or wind intensity in wind turbines. This study provides a strategic framework to balance the supply and demand fluctuation for RES-based microgrids in an isolated area. By proposing the NILM, the load can be separately monitored for load shedding later when a critical condition occurs. NILM based on Data-Flow Programming (BDT) offers an appropriate NILM method to be applied to isolated areas. The infrastructure needed to implement the proposed method is related to building a monitoring station equipped with a set of computers and sensors so that the NILM algorithm can proceed. The load shedding and load priority algorithm are also presented in this study to achieve customer satisfaction. The method was developed by considering the limitations of infrastructure in communication, transportation, and other technologies in isolated areas. Therefore, it is necessary to have good cooperation between grid operators and consumers so that the proposed method can work properly. [ABSTRACT FROM AUTHOR]

Load shedding control is an emergency control measure to maintain the frequency stability of the power system. Most of the existing load shedding methods use the extensive form of directly cutting off the outlet of the substation, featuring low control accuracy and high control cost. A network reconfiguration technique can adjust the topology of the distribution network and offers more optimization space for load shedding control. Therefore, this paper proposes a reconfiguration–load shedding coordination optimization scheme to reduce the power loss caused by load shedding control. In the proposed method, a load shedding mathematical optimization model based on distribution network reconfiguration is first established. The tie switches and segment switches in the distribution network are used to perform the reconfiguration of the distribution network, and the load switches are adopted to realize the load shedding. To improve the solving efficiency of the model, a solving strategy that combined a minimum spanning tree algorithm with an improved genetic algorithm is trailed to address the nonlinear and nonconvex terms. The application of the proposed method and model are finally verified via the IEEE 33 bus system, and the advantages in reducing the loss cost and the number of outage users are accordingly proven. [ABSTRACT FROM AUTHOR]

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods. [ABSTRACT FROM AUTHOR]

Summary: To minimize the emissions from the production of biofuels, one aim is to reduce or completely replace the amount of fossil fuels used for internal process energy. A conceivable solution is the use of volatile renewable energy sources such as solar and wind. The fluctuation of these energy sources in their production inevitably leads to a transformation to flexible power consumption, commonly referred to as demand side management (DSM). In current research, a wide range of processes has been identified to be suitable for DSM application. Although biorefineries have not yet been tested for DSM application, it is noteworthy that many of the DSM suitable processes are employed in biofuel plants. Thus, this contribution offers a comprehensive overview of DSM options drawn from literature with a special focus on process steps, which have been analysed for operational and capacity flexibility and which are found in or are transferable to biorefinery systems. By identifying process steps in biofuel production that can be operated flexibly, this extensive literature study helps to find technical restrictions limiting the overall process flexibility. The scope of this contribution is to create an overview of which processes in biofuel production can be considered for DSM use. [ABSTRACT FROM AUTHOR]

The Under-Frequency Load Shedding (UFLS) scheme is an emergency measure in place to prevent system collapse in the event of a large generation deficit relative to demand at any moment in time. However, the effectiveness of traditional UFLS schemes may be compromised as network power flows change due to the growth of distributed generation (DG) and a reduction in system inertia. This study has shown some of the issues affecting the UFLS protection system with rising installed capacities of DG in the Great Britain power system. Simulation results show how the effectiveness of the scheme is challenged with current and future expected DG installed capacities and the risk of over shedding demand due to the scheme’s current settings is evaluated. Many proposed improvements to traditional load shedding schemes are based on upgrades to network monitoring and communication systems, which are not yet fully available. This study evaluates more readily available solutions, including relocation of under-frequency relays and reducing the time delay of the scheme, which may provide interim improvements to the UFLS scheme, based on a case study in GB. [ABSTRACT FROM AUTHOR]

In order to analyze the influence of uncertainty and an operation strategy on the reliability of a standalone microgrid, a reliability evaluation method based on a sequential Monte Carlo (SMC) simulation was developed. Here, the duty cycles of a microturbine (MT), the stochastic performance of photovoltaics (PV), and wind turbine generators (WTG) were considered. Moreover, the time-varying load with random fluctuation was modeled. In this method, the available capacity of an energy storage system (ESS) was also comprehensively considered by the SMC simulation. Then, the reliability evaluation framework was established from the perspectives of probability, frequency, and duration, and reliability evaluation algorithms under different operation strategies were formulated. Lastly, the influence of WTG and PV penetration and equipment capacity on the reliability was evaluated in the test system. The results showed that the complementary characteristics of wind and solar and the enhancement of the equipment capacity can both improve the reliability; but, with the increase in the penetration rate of WTG and PV, more ESS capacity is needed to cope with the randomness of WTG and PV. In addition, load shedding minimization strategies can minimize the probability and the number of reductions and achieve optimal reliability, which can provide a reference for the formulation of microgrid operation strategies. [ABSTRACT FROM AUTHOR]

Renewable generation has grown rapidly these years due to its advantages in low environmental impacts. Stand-alone microgrid with renewable generation and energy storage is a promising option in remote areas beyond the reach of an existing power grid. This paper proposes a multiobjective optimization model to co-optimize the sizes of renewable generation and energy storage in stand-alone microgrids, which minimizes the load shedding risk and the total investment cost. The load shedding degree is obtained by an optimal operation problem and measured by shortfall risk, considering the uncertainties of demand and renewable resources. To deal with the inaccurate empirical distribution, distributionally robust shortfall risk is further established under a Wasserstein-metric-based ambiguity set, and an equivalent distributionally robust formulation is obtained. Then a conservative approximation is developed, and the resulting problem appears to be a bi-objective linear program. The Pareto frontier is proven to be piecewise linear, and the analytical expression of the Pareto frontier is derived. Case studies demonstrate the effectiveness and performance of the proposed method. [ABSTRACT FROM AUTHOR]

Despite the significant slowdown of economic activity in South Africa by virtue of the COVID-19 outbreak, load shedding or scheduled power outages remained at a high level. The trend of rising load-shedding hours has persisted throughout most of the year 2022. Operational issues within the South African power utility inflamed the unpredictable nature of generation capacity, resulting in unscheduled outages at several generating units, mostly due to multiple breakdowns. To forestall substantial spikes in energy costs, an increasing number of enterprises and homeowners have started to gradually adopt renewable energy technologies to sustain their operational demand. Therefore, there is an increase in the exploration and investment of battery energy storage systems (BESS) to exploit South Africa's high solar photovoltaic (PV) energy and help alleviate production losses related to load-shedding-induced downtime. As a result, the current work presents a comprehensive and consequential review conducted on the BESS specifically for solar PV application and in the South African context. The research investigations carried out on BESS for PV application are crucially examined, drawing attention to their capacities, shortcomings, constraints, and prospects for advancement. This investigation probed several areas of interest where the BESS-PV scheme is adopted, viz., choice of battery technology, mitigating miscellaneous power quality problems, optimal power system control, peak load shaving, South African BESS market and status of some Real BESS-PV projects. The techno-economic case scenario has been proposed in the current research and results yield that lithium-ion batteries are more viable than Lead–acid batteries. [ABSTRACT FROM AUTHOR]

The Under Frequency Load Shedding (UFLS) is the last automated action to restore power system frequency stability when a severe contingency causes a significant drop in the frequency of interconnection or islanded areas. In this paper, a novel probabilistic algorithm that considers the wind turbine frequency response is proposed to design and optimize the UFLS scheme. The uncertainties of power systems such as wind speed, hourly load, inertia time constant, and generation losses are taken into account. The Monte-Carlo Simulation (MCS) method is used to model the power system uncertainties. Additionally, a novel method is developed to calculate the power system frequency response and accelerate the multistage UFLS scheme optimization processes. In this method, a time-independent formulation for power system frequency response is utilized to reduce the computational complexity. More than Twenty different UFLS plans are designed using the proposed probabilistic algorithm. Besides, some time-domain simulations are carried on the modified IEEE 39-bus test system to investigate the performance of the designed UFLS schemes. Results indicate that designed UFLS schemes provide stable frequency response and improve the power system frequency stability. [ABSTRACT FROM AUTHOR]

This paper presents a novel protection strategy for assuring power-system frequency stability. After many years of use, the existing (static) under-frequency load-shedding concept has proven to be reliable and fast. However, it does not adapt well to volatile operating conditions, which might often lead to a system collapse. This developed into a major obstacle, since operational conditions of modern power systems are persistently subjected to fast and unpredictable changes. Therefore, there is a need to introduce a new capability in under-frequency load-shedding that would be able to fine-tune the mismatch in active power in any conditions. From the practical aspect, a systematic implementation of improvements should not threaten system stability. In terms of cost, a minimum intervention in the existing concept is desirable. This is why we developed an effective upgrade of the existing concept by introducing a specialized group of intelligent electronic devices supported by a principal component analysis technique. This group should be operated alongside the existing solution to monitor the actual frequency conditions and to take over power-balancing when fine-tuning is required. This supports the existing load-shedding and improves the process of frequency stabilization. [ABSTRACT FROM AUTHOR]

Emergency frequency control is one of the most critical approaches to maintain power system stability after major disturbances. With the increasing number of grid-connected renewable energy sources, existing model-based methods of frequency control are facing up with challenges of computational speed and scalability for large-scale systems. In this paper, the emergency frequency control problem is formulated as a Markov Decision Process and solved through a novel distributional deep reinforcement learning (DRL) method, namely the distributional soft actor critic (DSAC) method. Compared with other reinforcement learning methods that only estimate the mean value, the proposed DSAC model estimates the distribution of value function over returns. This advancement can lead to more insights and knowledge for the agent, with the benefit of a much faster and more stable learning process, and the improved frequency control performance. The simulation results on IEEE 39-bus and IEEE 118-bus systems demonstrate the effectiveness and robustness of proposed models, as well as the advantage compared to other state-of-the-art DRL algorithms. [ABSTRACT FROM AUTHOR]

Electrification is expected to decarbonise transportation and forms part of the agenda to delay climate change. Electric vehicle sales have ballooned and production of combustion engines will stop soon. In sub-Saharan Africa the transition is slow. Minibus taxis carry more than 70% of commuters and little is known about their electrification requirements. Electrical demand planning is better with vehicle-based data than with passenger-based data. Stationary times provide ample time for taxis to recharge from the grid and solar, but the latter requires substantial stationary battery capacity to negate grid-impacting fast charging. Taxi energy requirements are approximately 200 kWh/day on average. [ABSTRACT FROM AUTHOR]

In order to improve their long-term viability, wave energy converters (WECs) need to be able to shed loads when a threshold wave condition is exceeded. As shown by Tom et al. (2016) [1], provision of adjustable flaps within the body of an oscillating surge wave energy converter (OSWEC) allows wave energy to pass through the device. A control system may then be able to open and close the flaps when waves approaching the device exceed preset thresholds. The variable-geometry OSWEC (VG-OSWEC) concept studied in this paper is a bottom-hinged, rectangular wave paddle with five flaps of elliptical cross-section embedded into the face of the paddle. System ID tests were conducted on this VG-OSWEC device at a 1:14 scale in a wave basin. Free decay tests showed that the damping was distinctly nonlinear when the flaps were fully open, and the natural frequency increased by 40% when compared with the flaps in a fully closed configuration. Tests with regular wave conditions were used to develop the response amplitude operator for the rotational motion about the hinge. These response amplitude operator results when compared with numerical simulations run using WEC-Sim/WAMIT and ANSYS AQWA, show strong agreement with the flap open and closed conditions. The regular-wave condition measurements also show that the wave excitation moment about the hinge was reduced by up to 60% when the flaps were fully open. The experiments serve to demonstrate the potential of the variable geometry design to shed loads and survive harsh ocean environments. [ABSTRACT FROM AUTHOR]

Energy communities have been designed to promote sustainable development in the form of improved and affordable energy access, sustainable generation, and social inclusion. As their legislative background continues to evolve, future upgrades are expected to increase the benefits that these novel energy concepts offer. In Austria, from 2024 onwards, it will be possible to participate in more than one energy community at the same time; as such, it is necessary to evaluate the potential benefits for participants and the existing electricity grid. Thus, in this study, an optimization model is proposed to allocate the demand and production of each participant, generation unit, and storage initially belonging to different communities that are implemented under the same distribution transformer and engage in multiple participation. Both energy- and grid-related costs are minimized, and the benchmark independent energy community case is compared with the novel multiple participation. The influence of participants' acceptance on providing flexibility (in the form of load shedding) is assessed through sensitivity analysis. The results show that, compared to the independent case, multiple participation could provide additional reductions in terms of emissions (3.5%), costs (up to 10%), and peak demand (up to 29%) at the transformer level. However, communities with higher generation shares could be individually disadvantaged compared to those with lower generation shares. Storage could also assist in reducing costs and peak demand, but at the cost of faster aging and with relatively small differences between the independent and multiple participation cases. [Display omitted] • Optimal allocation of energy shares between different energy communities. • Comparison between independent ECs and multiple participation ECs. • Analysis on the peak load reduction behind the transformer and single-EC level. • Sensitivity of the grid to different load shedding acceptance. • Analysis on the number of cycles performed by the storages. [ABSTRACT FROM AUTHOR]

• A novel method for identifying the electric loads that have more impact on the stabilization of the post-fault dynamics of nodal frequencies is proposed. • The proposal determines how the direction and magnitude of nodal loads must change to maintain the post-disturbance frequency stability. • A new sequential formulation of the FSC-OLS problem is proposed to optimally perform a selective LS. • The effectiveness and accuracy of the proposed approach to optimally perform a selective LS is successfully demonstrated by numerical examples. This paper proposes an optimal selective under-frequency load shedding strategy to perform a corrective control action that prevents the loss of frequency stability. The proposal decomposes this optimal load shedding problem into two mutually connected subproblems: the frequency stability analysis and the optimal load shedding. These two subproblems are sequentially solved until the minimum load shedding needed to recover the center-of-inertia frequency close to its nominal value is determined. The link between those two subproblems is a frequency performance index, which indicates the frequency deviation at each generation node with respect to the nominal frequency value. To selectively perform the load shedding, two novel methods are developed to identify which nodal loads most affect the frequency dynamics and to determine how the selected loads must be shed to return the frequency close to its nominal value at the maximum rate of change. The effectiveness of the proposed method is numerically demonstrated in two benchmark power systems. [ABSTRACT FROM AUTHOR]

• An optimal load shedding model for IEDCSs with detailed topology and transmission energy loss is established. • The evaluation method for reliability indices is improved considering the thermal inertia characteristics and the transportation delay. • The sequential MCS is employed to describe the time-varying characteristics of load and the fault and repair characteristics of equipment. The tight interactions between energy sectors increase the risk of affecting integrated energy systems (IESs); therefore, reliability assessment is important for identifying vulnerability and improving security in IESs. In the literature, the network topology, transmission loss, and distinguishing thermal dynamics in IESs are seldom addressed for reliability assessment, rendering the results unreliable. To bridge this gap, an integrated electricity and district cooling model is formulated for IESs that involves elaborate network topologies and transmission losses to reflect their impacts on reliability characteristics. In addition, the evaluation of reliability indices for district cooling systems is improved by considering the thermal inertia characteristics of buildings and the transportation delay of circulating water to characterize the reliability levels of district cooling systems more accurately. Numerical results using a 12-node case validate the improved accuracy of the proposed reliability assessment method and reveal the potential of district cooling systems for enhancing the reliability of power systems. [ABSTRACT FROM AUTHOR]

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment. [ABSTRACT FROM AUTHOR]

Despite increased global energy supply, many households have insufficient access to energy in developing countries. Power utility companies, mostly parastatals, fail to cope with the burgeoning energy demand. This results in load shedding and impacts strongly associated with people's socio-economic classification. With poorer residential areas disproportionately adversely affected, socio-spatial segregation across urban space is evident. Using the socio-spatial segregation framework, this study employed convergent parallel mixed methods to examine effects of load shedding on households in Kitwe. Research objectives were to (1) investigate how household SMEs were affected by load shedding (2) find out dominant energy mixes in the economically differentiated residential areas (3) establish if there are any theoretical implications of the effects of load shedding on the residential area categorisation. A questionnaire was administered to 510 low, middle and high-income residential area households. Data were analysed using content analysis, descriptive and inferential statistics. Results reveal similar socio-economic effects across all groups except household income for middle and high-income areas. Small businesses experienced reduced profits with increased business costs especially for high-income areas. In conclusion, socio-spatial segregation created little difference in social and economic impacts at household level implying reducing inequalities due to increasing informal sector activities. [ABSTRACT FROM AUTHOR]

Transient stability of a power system can be significantly affected by wind power generators due to their stochastic power output and complex dynamic characteristics. This paper proposes a robust optimization approach for coordinating generation dispatch and emergency load shedding against transient instability under uncertain wind power output. The problem is modelled as a two-stage robust optimization (TSRO) model considering transient stability constraints, where the first-stage is to optimize the generation dispatch (preventive control) before a contingency and the second-stage decision is the emergency load shedding (emergency control) after the contingency occurrence under the worst case of wind power variation. To solve this TSRO problem, this paper also proposes a solution algorithm which integrates transient stability assessment and transient stability constraint construction in a column and constraint generation framework. The proposed method is validated on the New-England 39-bus system and the Nordic32 system, which shows high computational efficiency and stability robustness against uncertain wind power. [ABSTRACT FROM AUTHOR]

This paper proposes a novel real-time adaptive under voltage load shedding (UVLS) method by using optimized fuzzy logic controllers to address fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability (STVIS) problems. An intelligent multi-input multi-output fuzzy logic load shedding controller is developed based on a short-term voltage recovery criterion (STVRC). For the controller, the magnitude deviation and the recovery time deviation of voltage trajectory are extracted as inputs, while the load shedding amount at the current load shedding stage and the time delay of the next stage are taken as outputs. Particle swarm optimization (PSO) technique is further employed for the tuning of the controller parameters to realize the voltage recovery requirement at the lowest cost of load shedding. The proposed UVLS method is formed by a group of the fuzzy logic controllers deployed at the load buses, each monitoring its own bus voltage and shedding load in closed-loop. Responding to the severity of voltage variations, the proposed method is able to adaptively determine the location, time and amount of load shedding in real-time to make the voltage satisfy the STVRC. The effectiveness of the proposed method compared to other methods is testified by simulations on the Nordic test system. [ABSTRACT FROM AUTHOR]

This paper presents a novel load shedding scheme with consideration of the active power ramping capability of Distributed Energy Resources (DERs) to address the challenges due to low inertia and diverse types of DERs in microgrids. In the paper, it is demonstrated that due to the small inertia in microgrids, even with sufficient reserve power, the frequency could rapidly drop to a low level and trigger the DERs’ under frequency protection (thus the total system collapse), if the reserve active power is not ramped up at a sufficient rate. The proposed load shedding scheme addresses this challenge by considering not only the DERs’ reserve, but also their speed in injecting active power to the system to determine the amount of load should be shed, so that critical frequency thresholds are not violated. The proposed load shedding scheme is tested using a realistic real time hardware-in-the-loop arrangement. The results show that the proposed scheme can correctly detect the cases when the DERs’ responses are too slow and trigger the required load shedding actions, thus effectively containing the frequency above the critical threshold. [ABSTRACT FROM AUTHOR]

One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system, and the obtained results indicate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A challenging problem facing AC-Quasi-Steady-State (AC-QSS) cascading failure models of power system is the divergence issue primarily stemming from voltage collapse phenomena. In reality, there are undervoltage load shedding (UVLS) relays, which aim to prevent such a collapse by shedding a pre-specified fraction of load at buses where the corresponding voltages fall below a threshold. However, capturing the UVLS action in QSS models is very difficult, because most of the time the model cannot generate an equilibrium below the voltage threshold due to divergence. To address this problem, current models have applied different variants of uniform load shedding (ULS) till convergence is achieved, which differ from the ground truth. In order to solve this, we propose a methodology that leverages the post-ULS load flow as a starting point when divergence occurs. In this condition, a sensitivity index coupled with the voltage magnitudes of buses is used to recognize the buses that are most prone to voltage collapse. The UVLS scheme is then applied to these buses. To verify the accuracy of the results, we also present a suitable dynamic cascade model with appropriate limits and protection details that can selectively capture UVLS action, thereby revealing the proximate ground truth. Predictions of the proposed model are validated against those of the dynamic model for representative cases in IEEE 118-bus system. In addition, results of the proposed model are contrasted with two ULS schemes on the 2383-bus Polish system. [ABSTRACT FROM AUTHOR]

Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver. [ABSTRACT FROM AUTHOR]