Electric Grid System Research Articles

Energy Cost-Efficient Task Positioning in Manufacturing Systems

A problem to determine a production schedule which minimises the cost of energy used for manufacturing is studied. The scenario assumes that each production task has assigned constant power consumption, price of power from conventional electrical grid system is defined by time-of-use tariffs, and a component of free of charge renewable energy is available for the manufacturing system. The objective is to find the most cost-efficient production plan, subject to constraints involving predefined precedence relationships between the tasks and a bounded makespan. Two independent optimisation approaches have been developed, based on significantly different paradigms, namely mixed-integer linear programming and tabu search metaheuristic. Both of them have been verified and compared in extensive computational experiments. The tabu search-based approach has turned out to be generally more efficient in the sense of the obtained objective function values, but advantages of the use of linear programming have also been identified. The results confirm that it is possible to develop efficient computational methods to optimise energy cost under circumstances typical of manufacturing companies. The set of numerous benchmark instances and their solutions have been archived and it can be reused in further research.

Fault detection scheme based on feeder buses impedance angles discriminator in smart microgrid

Abstract Smart Grid (SG) or a modern electric grid system is becoming a priority for most developed countries. SG describes the integration of elements connected to the electric grid with an information infrastructure through intelligent communication systems to offer numerous benefits for both the provider and consumers of electricity. Thus, the conventional protection schemes have to be moderated for a reliable operation. This paper proposes a new central protection scheme for typical SG with hybrid distributed generations and energy storage. The proposed algorithm employs the available data through the SG communication facilities in three sequentially steps, namely fault detection, bus current direction and faulty feeder selection followed by the proper relay action. Line terminal voltage and current signals are continuously recorded, sampled and transmitted to the main protection centre in form of movable window patterns. The bus impedance angles are estimated and its deviations are tracked. Accordingly, a proposed discriminator is applied to evaluate and identify each bus flow direction during the fault either in forward or reverse direction with respect to the installed protective relay. Finally, the faulty feeder is selected and the required relay action is taken by tripping for the internal fault feeder and blocking for external faults.

Availability importance measures of components in smart electric power grid systems

The emergence of the smart grid has brought great innovation in the new distribution power system, facilitated a green and sustainable energy-based society, and mitigated the growing energy crisis. The smart grid is regarded as the next generation electrical power grid. It not only improves the power distribution systems with the techniques of distributed generation, but also makes the systems more complicated than the traditional ones. Thus it is important to evaluate the availability of smarts grids and to indicate the vulnerable parts of the systems. In this paper, a hierarchical model consisting of a layered fault tree (FT) and continuous-time Markov chains (CTMCs) is presented to model a smart grid system with two different power supply modes. We also analyze the component importance of the system, aiming to find the weak parts of the system thereby improving the system design. The importance analysis of components is based on parametric sensitivities and binary decision diagram (BDD) representation for the FTs. In a numerical illustration, we quantify the availability of the system with two power supply modes and also evaluate the importance of all components in the system.

Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy

Reducing peak power demand in a building can reduce electricity expenses for the building owner and contribute to the efficiency and reliability of the electrical power grid. For the building owner, reduced expenses come from the reduction or elimination of peak power charges on electricity bills. For the power system operator, reducing peak power demand leads to a more predictable load profile and reduces stress on the electric grid system. We present a computationally inexpensive, dynamic, and retrofit-deployable control strategy to effect peak load reduction and load shaping. The effectiveness of the control strategy is examined in a simulation with 80 air-conditioning units and 40 refrigeration units. The results show that a peak demand reduction of 60 kW can be achieved relative to peak demand in a typical set point–based approach. The proposed strategy was deployed in a gymnasium building with four rooftop HVAC units, where it showed over 15% peak demand (kW) reduction savings while maintaining or lowering energy consumption (in kilowatt-hours) relative to the set point–based thermostat controls.

Modeling the Transient Behavior of Gas Turbines

Abstract As a consequence of the increasing share of volatile renewable energy sources such as wind and solar in present-day electrical grid systems, time variations of the power demand for fossil fuel plants can become more sudden. Therefore, an ability to respond to sudden load changes becomes an important issue for power generation gas turbines. This paper describes a real-time model for predicting the transient performance of gas turbines. The method includes basic transient phenomena, such as volume packing and the heat transfer between the working fluid and the structural elements. The dynamics of components are quantified by solving ordinary differential equations with appropriate initial and boundary conditions. Compressor and turbine operating points are determined from corresponding performance maps previously calculated using sophisticated aerodynamic, through-flow codes. This includes a sufficient number of such characteristics to account for the variations in speed and machine geometry. The developed dynamic model was verified by comparison of simulation results with experimentally recorded operating parameters for a real engine. This includes the start-up sequence and the load changes. Additional simulation covers the system response to a step increase in fuel flow. The simulation is carried out faster than the real process.

Detection of unregistered electric distribution transformers in agricultural fields with the aid of Sentinel-1 SAR images by machine learning approaches

Electric Distribution Transformers (EDTs) are used in many areas for converting the voltage used in the transmission lines to a suitable voltage. One of the specific use areas of EDTs is to provide irrigation of agricultural areas. However, there is a great difficulty in detection of unregistered EDTs in these fields. The illegal usage of them damages the electric distribution companies and cause serious damages by disrupting the electricity grid system. To overcome these problems, Sentinel-1 SAR images give good results when used together with machine learning approaches. This work was carried out on agricultural lands in Diyarbakir province, Turkey and it consists of several stages. In first place, GPS coordinates of 178 sample points were recorded and 32 Sentinel-1 SAR images were obtained between March-May & 2016–2018. In the second phase, backscattering coefficients in different polarizations were generated for each point in these dates. The features obtained from 32 Sentinel-1 SAR images were then added successively and a feature vector with a total length of 64 was produced for each sample point. After that, different machine learning techniques [Extreme Learning Machine (ELM), K-Nearest Neighbor (K-NN), Naive Bayes Algorithm (NBA) and Support Vector Machine (SVM)] were applied to the obtained datasets and ELM was determined as best machine learning approach. A machine learning based feature selection (ELM-FS) method was then applied to all feature vectors and the most relevant features were determined according to their impact values. In the last stage, feature sets obtained from ELM-FS method were used separately as inputs of ELM technique and the unregistered EDTs were successfully detected with 85.47% mean accuracy.

Security Assumptions for Ubiquitous Secure Smart Grid Infrastructure using 2 Way Peg Blockchain and Fuzzy Specifications

The smart grid cyber-physical system and advanced metering infrastructure is an immensely growing term in industrial research based on the electrical power grid system and has uncovered an efficient paradigm for the power generation, transmission, and for the expenditure management as well. These systems allow home area networks and advanced metering infrastructure to communicate bi-directionally. False data injection is a new and climacteric security threat to the power grid to tamper such important data. However, these communication channels that the energy service interfaces provide two-way communication according to the central control unit. Thus, for the security and robustness of the grid, it is critical to separate the false or infected data. The existing protocol on this problem is based on state estimation which is less secure and computationally expensive. In this paper, we propose a 2-way peg blockchain security system by providing a digital signature scheme between home area network device (Smart Meter) and AMI server to get authorization, providing an infected data detection algorithm based on fuzzy rule specification and 2WP based blockchain protocol and finally infected performance reputation updating algorithm by computing the probability distribution of uncouth level to detect the infected or compromised the. We get more efficiency and security by performing a detailed analysis.

Reducing the computational burden of a microgrid energy management system

As renewable technology advances and decreases in cost, microgrids are becoming an appealing means of distributed generation both for isolated communities and integrated with existing electrical grid systems. Due to their small size, however, microgrids may have financial limitations which preclude them from using commercial software to optimize control of their assets. Open-source optimization solvers are a viable alternative, but increase computation time. This work expands on a rolling horizon optimization framework for economic dispatch within an existing residential microgrid located in Hoover, Alabama. The microgrid has an open-source solver requirement and a need for quick solution time on a rolling horizon as opposed to a day-ahead commitment. We present a method of reducing integer variables by relaxation which completes two goals: reduction in computation time for real-time operations, and reduction in daily operational cost for the microgrid. Seasonal data for load and photovoltaic (PV) power was also collected from the microgrid to facilitate simulation testing. Computation time was successfully reduced using multiple variations of the relaxation method, while obtaining solution quality with operational cost similar to or better than the original model.

Assessment of Commercial Building Lighting as a Frequency Regulation Resource

This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.

Towards Energy Efficient Load Balancing for Sustainable Green Wireless Networks Under Optimal Power Supply

The enormous growth in the cellular networks and ubiquitous wireless services has incurred momentous energy consumption, greenhouse gas (GHG) emissions and thereby, imposed a great challenge to the development of energy-efficient sustainable cellular networks. With the augmentation of harvesting renewable energy, cellular base stations (BSs) are progressively being powered by renewable energy sources (RES) to reduce the energy crisis, carbon contents, and its dependency on conventional grid supply. Thus, the combined utilization of renewable energy sources with the electrical grid system is proving to be a more realistic option for developing an energy-efficient as well as an eco-sustainable system in the context of green mobile communications. The ultimate objective of this work is to develop a traffic-aware grid-connected solar photovoltaic (PV) optimal power supply system endeavoring the remote radio head (RRH) enabled heterogeneous networks (HetNets) aiming to minimize grid energy consumption and carbon footprint while ensuring long-term energy sustainability and energy efficiency (EE). Moreover, the load balancing technique is implemented among collocated BSs for better resource blocks (RBs) utilization and thereafter, the performance of the system is compared with an existing cell zooming enabled cellular architecture for benchmarking. Besides, the techno-economic feasibility of the envisaged system has been extensively analyzed using HOMER optimization software considering the dynamic nature of solar generation profile and traffic arrival rate. Furthermore, a thorough investigation is conducted with the help of Monte-Carlo simulations to assess the wireless network performance in terms of throughput, spectral efficiency (SE), and energy efficiency as well under a wide range of design scenarios. The numerical outcomes demonstrate that the proposed grid-tied solar PV/battery system can achieve a significant reduction of grid power consumption yielding up to 54.8% and ensure prominent energy sustainability with the effective modeling of renewable energy harvesting.

The Conducted Emission Attenuation of Micro-Inverters for Nanogrid Systems

Road lighting systems require a significant amount of electric energy. To compensate for the utilized energy, the concept of a nanogrid road lighting system is presented. A solar panel is installed on the top of a lighting pole to generate electric power. In this research, a photovoltaic simulator (PV simulator), which is used to simulate solar behavior such as current, voltage, and power based on temperature and solar irradiance levels, is employed to replace a solar panel. In the nanogrid system, grid-connected and stand-alone micro-inverters are employed to convert the electric power. The inverters comprise switching devices that can generate electromagnetic interference (EMI) when operating, which is harmful to the grid system and the electrical equipment. In general, EMI has been studied and reduced in electrical appliances, which only receive electric power. However, for the nanogrid system, which supplies electricity to the grid system, there is less study on the EMI topic because the usage is still not widespread. In the future, the nanogrid system will be widely used delivering high power directly into the electrical grid system. Therefore, the study and attenuation of EMI in the nanogrid system are very promising. Conducted emission (CE) is one form of EMI that flows through a cable connecting several appliances in the frequency range of 150 kHz to 30 MHz. CE of grid-connected and stand-alone micro-inverters have high levels in the low-frequency range between 150 kHz–5 MHz and then decreases steadily. CE attenuation is important for this inverter in a solar power system. This research studies the effect of CE mitigation on the nanogrid system. The result is compared with the Comité International Spécial des Perturbations Radio (CISPR) 14-1 standard. Finally, the passive EMI filter can reduce CE and meets the CISPR 14-1 standard.

Lightweight sustainable intelligent load forecasting platform for smart grid applications

With the global electricity demand witnessing a 3.1 percent jump in 2017, there is an increasing need for incorporating intermittent renewable energy sources and other alternative supply/demand management strategies into the supply grid networks. Short-term load forecasting models enable prediction of future power consumption, thereby encouraging shifting of loads and optimizing the use of stochastic power sources and stored energy. To make the electric grid system smart and sustainable, two-way communication between the utility and consumers must be set up and the working equipment must respond digitally to the quickly changing electric demand. The proposed work exploits the power of embedded systems to design a low-cost solution for interconnecting electrical and electronic devices, controlled by the intelligent Internet of Things (IoT) paradigms. This work primarily focuses on implementing standard regression and machine learning-based architectures for smart grid load analysis and forecasting. A state of the art ecosystem for a portable load forecasting device is proposed by means of low-cost, open-source hardware that is experimentally found to be functioning with a high degree of accuracy. Further, the performance of the classical and advanced machine learning models, emulated on the device, are analyzed on the basis of various parameters, including error percentage, execution time, CPU core temperatures, and resource utilization. Overall impressive performance is demonstrated by some specific machine learning models which are considered to be suitable for the proposed framework.

Performance Research on Hybrid System Using MATLAB Simulink

This paper proposes the performance analysis of hybrid system using MATLAB simulation. In the present era, the increasing energy demand due to increase in the large number of loads depress the electrical grid system to fulfil the energy demand of each and every customer. It is impossible for the grid system to do so, therefore to get rid off from these issues a renewable energy-based hybrid system is developed that fulfils the energy demand in most efficient way without undergoing in the process of load shedding. The system is design in the MATLAB Simulink and the result obtained is compared to improve the efficiency of the system.

Modeling Electrical Grid Resilience Under Hurricane Wind Conditions With Increased Solar and Wind Power Generation

This paper develops models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations using Monte-Carlo simulation, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data are used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study is developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the NREL Futures Study 2050 Texas scenarios for 50% and 80% renewable energy.

Probability-driven transmission expansion planning with high-penetration renewable power generation: A case study in northwestern China

The incorporation of power generation derived from renewable energy sources, or renewable power generation (RPG), into conventional electric power grids has been rapidly increasing on a large scale in recent years. However, this process can be expected to inevitably increase the uncertainty associated with transmission line investment owing to the inherent uncertainty associated with RPG. While robust transmission expansion planning (RTEP) has been commonly employed for optimizing transmission line investments, this method suffers from serious disadvantages such as the neglect of available RPG probability information, overly conservative solutions, and exceedingly time consuming solution processes. The present work addresses these disadvantages by modeling the probability of RPG uncertainty according to RPG output probabilities obtained over a long-term planning horizon based on available historical data using a hybrid probability uncertainty set constructed using 1-norm and ∞-norm metrics. A probability-driven RTEP model is then proposed to obtain an optimal investment strategy that provides a security guarantee under the worst-case RPG probability distribution, while alleviating the need for overly conservative solutions with high total expansion costs. In addition, a modified column-and-constraint generation algorithm is developed to solve the proposed tri-level probability-driven RTEP model, where the large-scale inner bi-level component of the optimization problem is decomposed into several small-scale linear models that can be solved in parallel. The proposed algorithm requires no dual variables in the inner problem and eliminates all highly non-convex bilinear terms like those obtained in conventional RTEP solution algorithms. This can effectively increase the computational speed of the solution process. The effectiveness, good applicability, and robustness of the proposed model and solution algorithm are demonstrated by numerical applications based on a Garver’s 6-bus test system and an existing electric grid system in northwestern China.

Long Life Fully Reversible Lithium-CO2 Battery

Although far less studied, lithium-CO2 (Li-CO2) batteries are attractive energy storage systems for fulfilling the demand for the future large-scale applications such as electric vehicles and grid systems due to their higher specific energy density (~1876 Wh/kg) compared to those of commonly used lithium-ion (~265 Wh/kg) and lead-acid (~30-40 Wh/kg) batteries. However, the major challenges with these batteries are the low cyclability and poor reversibility of discharge products (e.g., Li2CO3 and carbon) during the battery cycling. An ideal system must operate in carbon neutral conditions in order to reversibly balance the electrochemical reactions during discharge and charge processes. In this work, using molybdenum disulfide nanoflakes as a cathode catalyst combined with an ionic liquid and dimethyl sulfoxide hybrid electrolyte, we have obtained a Li-CO2 battery with a long cycle life while maintaining a carbon neutrality in the system. The battery shows a superior cycle life of 500 for a fixed 500 mAh/g capacity per cycle and a remarkable deep discharge capacity of 60,000 mAh/g, which are by far the best cycling stability and highest capacity reported in Li-CO2 batteries, respectively. The presented Li-CO2 battery system demonstrates for the first time that C-O bond making and breaking chemical transformations can be used in energy storage systems with a long cycle life, in addition to the widely studied alkali metal (Li, Na, K) – oxygen bond making and breaking transformations. Theoretical calculations are used to provide insight into the reaction mechanisms for the reversible formation and decomposition of the products during the discharge and charge processes. The achievement of a reversible, long cycle life Li-CO2 battery helps to improve Li-CO2 chemical performance and energy storage systems.

A Review of Cuckoo Search Algorithm Based Optimal Siting and Sizing of Shunt Capacitors in Radial Distribution Systems

Compensating reactive power deficiency on power grids is a central concern in the distribution of energy management systems. Several approaches have been adopted over time to minimize the total real power loss and enhancing bus voltage profile. Shunt capacitor has been used from time immemorial for addressing issue of reactive power compensation at the distribution end of power systems, and the extent of benefits derivable from its usage depend solely on correct siting and sizing. To this effect, meta-heuristic algorithms are promising optimization tools for achieving these objectives. This paper, therefore, presents a comprehensive review of cuckoo search algorithm based on optimal siting and sizing of shunt capacitors in radial distribution systems. The suitability, in addition to strengths and weakness of each approaches reported in the reviewed articles have been painstakingly x-rayed. Based on the review, it was observed that a two-stage approach is always adopted in the compensation process: the pre-selection of potential or sensitive nodes and the optimal sizing of shunt capacitors needed for the compensation. For the pre-location, Voltage Stability Index and Loss Sensitivity Factor were found to be comparatively less complex and highly suitable techniques. Another cogent discovery from this review is that less attention has been drawn to the use of cuckoo search algorithm by Nigerian researchers. Therefore, regarding Nigerian electric grid system, the use of cuckoo search algorithm in reactive power support presents a research gap for further investigations.

Simulation of a 100-MW solar-powered thermo-chemical air separation system combined with an oxy-fuel power plant for bio-energy with carbon capture and storage (BECCS)

The combination of concentrated solar power–chemical looping air separation (CSP-CLAS) with an oxy-fuel combustion process for carbon dioxide (CO2) capture is a novel system to generate electricity from solar power and biomass while being able to store solar power efficiently. In this study, the computer program Advanced System for Process Engineering Plus (ASPEN Plus) was used to develop models to assess the process performance of such a process with manganese (Mn)-based oxygen carriers on alumina (Al2O3) support for a location in the region of Seville in Spain, using real solar beam irradiance and electricity demand data. It was shown that the utilisation of olive tree prunings (Olea europaea) as the fuel—an agricultural residue produced locally—results in negative CO2 emissions (a net removal of CO2 from the atmosphere). Furthermore, it was found that the process with an annual average electricity output of 18 MW would utilise 2.43% of Andalusia’s olive tree prunings, thereby capturing 260.5 k-tonnes of CO2, annually. Drawbacks of the system are its relatively high complexity, a significant energy penalty in the CLAS process associated with the steam requirements for the loop-seal fluidisation, and the gas storage requirements. Nevertheless, the utilisation of agricultural residues is highly promising, and given the large quantities produced globally (~ 4 billion tonnes/year), it is suggested that other novel processes tailored to these fuels should be investigated, under consideration of a future price on CO2 emissions, integration potential with a likely electricity grid system, and based on the local conditions and real data.

Forecasting Intra-Hour Imbalances in Electric Power Systems

Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators. This is due to the increasing complexity of the electric power grid system with the intermittent renewable production as one of the contributors. A beforehand information about an occurring imbalance can help the transmission system operator to adjust the production plans, and thus ensure a high security of supply by reducing the use of costly balancing reserves, and consequently reduce undesirable fluctuations of the 50 Hz power system frequency. In this paper, we introduce the relatively new problem of an intra-hour imbalance forecasting for the transmission system operator (TSO). We focus on the use case of the Norwegian TSO, Statnett. We present a complementary imbalance forecasting tool that is able to support the TSO in determining the trend of future imbalances, and show the potential to proactively alleviate imbalances with a higher accuracy compared to the contemporary solution.

Power variability of tidal-stream energy and implications for electricity supply

Temporal variability in renewable energy presents a major challenge for electrical grid systems. Tides are considered predictable due to their regular periodicity; however, the persistence and quality of tidal-stream generated electricity is unknown. This paper is the first study that attempts to address this knowledge gap through direct measurements of rotor-shaft power and shore-side voltage from a 1 MW, rated at grid-connection, tidal turbine (Orkney Islands, UK). Tidal asymmetry in turbulence parameters, flow speed and power variability were observed. Variability in the power at 0.5 Hz, associated with the 10-min running mean, was low (standard deviation 10–12% of rated power), with lower variability associated with higher flow speed and reduced turbulence intensity. Variability of shore-side measured voltage was well within acceptable levels (∼0.3% at 0.5 Hz). Variability in turbine power had <1% difference in energy yield calculation, even with a skewed power variability distribution. Finally, using a “t-location” distribution of observed fine-scale power variability, in combination with an idealised power curve, a synthetic power variability model reliably downscaled 30 min tidal velocity simulations to power at 0.5 Hz (R2 = 85% and ∼14% error). Therefore, the predictability and quality of tidal-stream energy was high and may be undervalued in a future, high-penetration renewable energy, electricity grid.