Large-scale Electric Systems Research Articles

Improved mountain gazelle optimizer based interactive distributed strategy to solving large scale OPF

This paper focuses on solving the multi-objective optimal power flow of large-scale power systems under critical loading margin stability with accuracy using a novel improved mountain gazelle optimizer (IMGO)-based flexible distributed strategy. Multi-shunt compensator-based flexible alternative current transmission systems (FACTS), such as SVC and STATCOM devices, are integrated at specified locations to exchange reactive power with the network. Several metaheuristic methods can solve the standard OPF related to small and medium test systems. However, by considering large-scale electric systems based on FACTS devices and renewable energy and by considering the operation under loading margin stability, the majority of these techniques fail to achieve a near-global solution because of the high dimension and nonlinearity of the problem to be solved. This study proposes the Multi-Objective OPF-Based Distributed Strategy (MO-OPFDS), a new planning strategy that optimizes individually and simultaneously various objective functions, in particular the total power loss (T∆P), and the total voltage deviation (T∆V). Standard MGO search is enhanced by automatically balancing exploration and exploitation throughout the search. The robustness of the proposed variant was validated on a large electric test system, the IEEE 118-Bus, and on the Algerian Network 114-Bus under normal conditions and at critical loading margin stability. The obtained results compared with several recent techniques clearly confirm the high performance of the proposed method in terms of solution accuracy and convergence behavior.

Assessing the impact of renewable energy penetration and geographical allocation on transmission expansion cost: A comparative analysis of two large-scale systems

Ambitious renewable energy development plans require an efficient electricity grid connection of massive generation capacity. Significant transmission network investments are considered necessary to support the energy transition. This paper analyses the transmission grid reinforcement requirements of two large-scale electricity systems with notably different geographical distributions of renewable resource availability and load density. The results indicate that optimal transmission expansion accounts for less than 10% of total annualised system investment (generation capacity + transmission grid). This is true even in transmission systems with significant existing bottlenecks. The study includes a sensitivity analysis of the location of renewable energy source (RES) capacity. The sensitivities examine the impact of varying the concentration of RES in resource-rich areas, as well as the proportion of RES capacity connected at the distribution level. Both sensitivities are evaluated over a common base case scenario. The results show that a higher concentration of RES capacity in resource-rich network zones can more than double the optimal transmission expansion requirements. In contrast, the implementation of distributed generation (DG) leads to the allocation of generation closer to demand centres, resulting in transmission grid savings of up to 30%. These effects are more pronounced in networks where RES capacity is located further away from major demand centres. This is because existing bottlenecks are exacerbated by higher RES concentration and relieved as the share of DG increases.

Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning

Power forecasting in large-scale electrical systems, comprising photovoltaic (PV), solar, and wind power, faces challenges due to geographical diffusion and temporal variations. Despite numerous studies, the disparity between predicted and actual generation remains a significant issue. This study utilizes historical power and atmospheric data from diverse plants, employing preprocessing techniques to enhance quality and reduce noise. K-Means clustering is applied to the dataset, optimizing deep learning training periods and increasing accuracy. A resilient hybrid deep learning model is proposed for microgrid (MG) power forecasting, encompassing preprocessing, model training, and assessment stages. Mathematical models for PV systems, battery storage, and wind systems, along with a K-means clustering algorithm, contribute to accurate forecasting. The recurrent neural network based on gated recurrent unit architecture outperforms traditional algorithms, demonstrating superior accuracy, and reduced errors in extensive experimental analyses. Pearson coefficients reveal associations between different power production forms, emphasizing the potential of hybrid renewable energy clusters to enhance forecasting. Case studies illustrate the partial controllability of concentrated solar power production, reducing overall renewable energy cluster unpredictability. The proposed method showcases the efficacy of the hybrid model in addressing challenges and improving accuracy in large-scale power forecasting.

A modelling framework to quantify the power system benefits from ocean energy deployments

It is understood that electricity generation from the waves and tides can be temporally and spatially offset from other, more established variable renewables, such as wind and solar. However, it is less well understood how this offsetting can impact on power system operation. A novel modelling framework has been developed to quantify the potential benefit of including higher proportions of ocean energy within large-scale electricity systems. Economic dispatch modelling is utilised to model hourly supply–demand matching for a range of sensitivity runs, adjusting the proportion of ocean energy within the generation mix. The framework is applied to a 2030 case study of the power system of Great Britain, testing installed wave or tidal stream capacities ranging from 100 MW to 10 GW. For all sensitivity runs it has been found that ocean energy increases renewable dispatch, reduces dispatch costs, reduces generation required from fossil fuels, reduces system carbon emissions, reduces price volatility, and captures higher market prices. For example, including 1 GW of wave displaces up to £137M and 128 ktCO2 over the year of dispatch modelled. Similarly, 1 GW of tidal stream displaces up to £95M and 87 ktCO2. When including 10 GW of ocean energy, dispatch costs reduce by up to 7% and carbon emissions reduce by up to 29%. This analysis has included the development and publication of open source models of the Great British power system.

Conservative Power Theory for Harmonic Voltage Responsibility Assignment

Discussions on the different methods for sharing responsibilities regarding harmonic distortions in power grids have received wide attention in the area of power quality. This paper proposes a methodology based on Conservative Power Theory (CPT) for responsibility assignment due to the harmonic voltage distortions present at the point of common coupling. First, a theoretical basis for analyzing the orthogonal current decomposition of CPT under non-sinusoidal conditions is presented. Next, using the frequency domain, a methodology is introduced to separate the contributions of the harmonic voltage distortions in the CPT's orthogonal currents. To illustrate the application of the proposed methodology, it is simulated a real large-scale electrical system composed of an energy utility and three industrial plants connected to the same common coupling point. Finally, the results obtained are compared to other method showing the effectiveness of the proposed approach.

A long-term unit commitment problem with hydrothermal coordination for economic and emission control in large-scale electricity systems

The paper describes a long-term scheduling problem for thermal power plants and energy storages. In addition, renewable energy sources are integrated by considering the residual demand. Besides the classical minimization of the production costs, emission-related costs are taken into account. Thereby, emission costs are determined by market prices for hbox {CO}_2 emission certificates (i.e., using the EU emissions trading system). For the proposed unit commitment problem with hydrothermal coordination for economic and emission control, an enhanced mixed-integer linear programming model is presented. Moreover, a new heuristic approach is developed, which consists of two solution stages. The heuristic first performs an isolated dispatching of thermal plants. Then, a re-optimization stage is included in order to embed activities of energy storages into the final solution schedule. The considered approach is able to find outstanding schedules for benchmark instances with a planning horizon of up to one year. Furthermore, promising results are also obtained for large-scale real-world electricity systems. For the German electricity market, the relationship of hbox {CO}_2 certificate prices and the optimal thermal dispatch is illustrated by a comprehensive sensitivity analysis.

Flow tracing as a tool set for the analysis of networked large-scale renewable electricity systems

The method of flow tracing follows the power flow from net-generating sources through the network to the net-consuming sinks, which allows to assign the usage of the underlying transmission infrastructure to the system participants. This article presents a reformulation that is applicable to arbitrary compositions of inflow appearing naturally in models of large-scale electricity systems with a high share of renewable power generation. We propose an application which allows to associate power flows on the grid to specific regions or generation technologies, and emphasizes the capability of this technique to disentangle the spatio-temporal patterns of physical imports and exports occurring in such systems. The analytical potential of this method is showcased for a scenario based on the IEEE 118 bus network.

Scaling of transmission capacities in coarse-grained renewable electricity networks

Network models of large-scale electricity systems feature only a limited spatial resolution, either due to lack of data or in order to reduce the complexity of the problem with respect to numerical calculations. In such cases, both the network topology, the load and the generation patterns below a given spatial scale are aggregated into representative nodes. This coarse-graining affects power flows and thus the resulting transmission needs of the system. We derive analytical scaling laws for measures of network transmission capacity and cost in coarse-grained renewable electricity networks. For the cost measure only a very weak scaling with the spatial resolution of the system is found. The analytical results are shown to describe the scaling of the transmission infrastructure measures for a simplified, but data-driven and spatially detailed model of the European electricity system with a high share of fluctuating renewable generation.

Backup flexibility classes in emerging large-scale renewable electricity systems

High shares of intermittent renewable power generation in a European electricity system will require flexible backup power generation on the dominant diurnal, synoptic, and seasonal weather timescales. The same three timescales are already covered by today’s dispatchable electricity generation facilities, which are able to follow the typical load variations on the intra-day, intra-week, and seasonal timescales. This work aims to quantify the changing demand for those three backup flexibility classes in emerging large-scale electricity systems, as they transform from low to high shares of variable renewable power generation. A weather-driven modelling is used, which aggregates eight years of wind and solar power generation data as well as load data over Germany and Europe, and splits the backup system required to cover the residual load into three flexibility classes distinguished by their respective maximum rates of change of power output. This modelling shows that the slowly flexible backup system is dominant at low renewable shares, but its optimized capacity decreases and drops close to zero once the average renewable power generation exceeds 50% of the mean load. The medium flexible backup capacities increase for modest renewable shares, peak at around a 40% renewable share, and then continuously decrease to almost zero once the average renewable power generation becomes larger than 100% of the mean load. The dispatch capacity of the highly flexible backup system becomes dominant for renewable shares beyond 50%, and reach their maximum around a 70% renewable share. For renewable shares above 70% the highly flexible backup capacity in Germany remains at its maximum, whereas it decreases again for Europe. This indicates that for highly renewable large-scale electricity systems the total required backup capacity can only be reduced if countries share their excess generation and backup power.

Optimal Wind Clustering Methodology for Adequacy Evaluation in System Generation Studies Using Nonsequential Monte Carlo Simulation

In this paper, several clustering algorithms are investigated in order to group together wind parks with close statistical behavior. Here, the proposed approach is practically founded on a fast incremental algorithm validated by a normalized principal component analysis combined with a k-means process. Both methods are practically based on the definition of a Pearson correlation coefficient. The advantage of such a clustering methodology is mainly perceptible in large-scale electrical systems with increased wind penetration. Indeed, it allows to group together highly correlated wind parks into the same cluster and to integrate them in a realistic way into a nonsequential Monte Carlo adequacy evaluation process. Here, the implemented clustering methodology is applied to 94 wind sites located in Occidental Europe. Then, in order to point out the efficiency of this clustering methodology that is afterwards combined with an original wind speed sampling process, an adequacy study is applied to the Roy Billinton Test System in the particular case of two wind clusters.

Power struggles: scientific authority and the creation of practical electricity before Edison

In 1882, Thomas Edison and his Edison Electric Light Company unveiled the first large-scale electrical system in the world to light a stretch of offices in a city. This was a monumental achievement, but it was not the beginning of the electrical age. The first electric generators were built in the 1830s, the earliest commercial lighting systems before 1860, and the first commercial application of generator-powered lights (in lighthouses) in the early 1860s. In Power Struggles, Michael Brian Schiffer examines some of these earlier efforts, both successful and unsuccessful, that paved the way for Edison. After laying out a unified theoretical framework for understanding technological change, Schiffer presents a series of fascinating case studies of pre-Edison electrical technologies, including Volta's electrochemical battery, the blacksmith's electric motor, the first mechanical generators, Morse's telegraph, the Atlantic cable, and the lighting of the Capitol dome. Schiffer discusses claims of and impracticality (sometimes hotly contested) made for these technologies, and examines the central role of the scientific authority--in particular, the activities of Joseph Henry, mid-nineteenth-century America's foremost scientist--in determining the fate of particular technologies. These emerging electrical technologies formed the foundation of the modern industrial world. Schiffer shows how and why they became commercial products in the context of an evolving corporate capitalism in which conflicting judgments of practicality sometimes turned into power struggles.